The Legend of the 'Unsolvable Math Problem'

A student mistook examples of unsolved math problems for a homework assignment and solved them., david mikkelson, published dec 3, 1996.

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A legend about the "unsolvable math problem" combines one of the ultimate academic wish-fulfillment fantasies — a student not only proves himself the smartest one in his class, but also bests his professor and every other scholar in his field of study — with a "positive thinking" motif that turns up in other urban legends: when people are free to pursue goals unfettered by presumed limitations on what they can accomplish, they just may manage some extraordinary feats through the combined application of native talent and hard work:

A young college student was working hard in an upper-level math course, for fear that he would be unable to pass. On the night before the final, he studied so long that he overslept the morning of the test. When he ran into the classroom several minutes late, he found three equations written on the blackboard. The first two went rather easily, but the third one seemed impossible. He worked frantically on it until — just ten minutes short of the deadline — he found a method that worked, and he finished the problems just as time was called. The student turned in his test paper and left. That evening he received a phone call from his professor. "Do you realize what you did on the test today?" he shouted at the student. "Oh, no," thought the student. I must not have gotten the problems right after all. "You were only supposed to do the first two problems," the professor explained. "That last one was an example of an equation that mathematicians since Einstein have been trying to solve without success. I discussed it with the class before starting the test. And you just solved it!"

And this particular version is all the more interesting for being based on a real-life incident!

One day in 1939, George Bernard Dantzig, a doctoral candidate at the University of California, Berkeley, arrived late for a graduate-level statistics class and found two problems written on the board. Not knowing they were examples of "unsolved" statistics problems, he mistook them for part of a homework assignment, jotted them down, and solved them. (The equations Dantzig tackled are more accurately described not as unsolvable problems, but rather as unproven statistical theorems for which he worked out proofs.)

Six weeks later, Dantzig's statistics professor notified him that he had prepared one of his two "homework" proofs for publication, and Dantzig was given co-author credit on a second paper several years later when another mathematician independently worked out the same solution to the second problem.

George Dantzig recounted his feat in a 1986 interview for the College Mathematics Journal :

It happened because during my first year at Berkeley I arrived late one day at one of [Jerzy] Neyman's classes. On the blackboard there were two problems that I assumed had been assigned for homework. I copied them down. A few days later I apologized to Neyman for taking so long to do the homework — the problems seemed to be a little harder than usual. I asked him if he still wanted it. He told me to throw it on his desk. I did so reluctantly because his desk was covered with such a heap of papers that I feared my homework would be lost there forever. About six weeks later, one Sunday morning about eight o'clock, [my wife] Anne and I were awakened by someone banging on our front door. It was Neyman. He rushed in with papers in hand, all excited: "I've just written an introduction to one of your papers. Read it so I can send it out right away for publication." For a minute I had no idea what he was talking about. To make a long story short, the problems on the blackboard that I had solved thinking they were homework were in fact two famous unsolved problems in statistics. That was the first inkling I had that there was anything special about them. A year later, when I began to worry about a thesis topic, Neyman just shrugged and told me to wrap the two problems in a binder and he would accept them as my thesis. The second of the two problems, however, was not published until after World War II. It happened this way. Around 1950 I received a letter from Abraham Wald enclosing the final galley proofs of a paper of his about to go to press in the Annals of Mathematical Statistics. Someone had just pointed out to him that the main result in his paper was the same as the second "homework" problem solved in my thesis. I wrote back suggesting we publish jointly. He simply inserted my name as coauthor into the galley proof.

Dr. Dantzig also explained how his story passed into the realm of urban legendry:

The other day, as I was taking an early morning walk, I was hailed by Don Knuth as he rode by on his bicycle. He is a colleague at Stanford. He stopped and said, "Hey, George — I was visiting in Indiana recently and heard a sermon about you in church. Do you know that you are an influence on Christians of middle America?" I looked at him, amazed. "After the sermon," he went on, "the minister came over and asked me if I knew a George Dantzig at Stanford, because that was the name of the person his sermon was about." The origin of that minister's sermon can be traced to another Lutheran minister, the Reverend Schuler [sic] of the Crystal Cathedral in Los Angeles. He told me his ideas about thinking positively, and I told him my story about the homework problems and my thesis. A few months later I received a letter from him asking permission to include my story in a book he was writing on the power of positive thinking. Schuler's published version was a bit garbled and exaggerated but essentially correct. The moral of his sermon was this: If I had known that the problem were not homework but were in fact two famous unsolved problems in statistics, I probably would not have thought positively, would have become discouraged, and would never have solved them.

The version of Dantzig's story published by Christian televangelist Robert Schuller contained a good deal of embellishment and misinformation which has since been propagated in urban legend-like forms of the tale such as the one quoted at the head of this page: Schuller converted the mistaken homework assignment into a "final exam" with ten problems (eight of which were real and two of which were "unsolvable"), claimed that "even Einstein was unable to unlock the secrets" of the two extra problems, and erroneously stated that Dantzig's professor was so impressed that he "gave Dantzig a job as his assistant, and Dantzig has been at Stanford ever since."

George Dantzig (himself the son of a mathematician) received a Bachelor's degree from University of Maryland in 1936 and a Master's from the University of Michigan in 1937 before completing his Doctorate (interrupted by World War II) at UC Berkeley in 1946. He later worked for the Air Force, took a position with the RAND Corporation as a research mathematician in 1952, became professor of operations research at Berkeley in 1960, and joined the faculty of Stanford University in 1966, where he taught and published as a professor of operations research until the 1990s. In 1975, Dr. Dantzig was awarded the National Medal of Science by President Gerald Ford.

George Dantzig passed away at his Stanford home at age 90 on 13 May 2005.

Sightings:   This legend is used as the setup of the plot in the 1997 movie Good Will Hunting . As well, one of the early scenes in the 1999 film Rushmore shows the main character daydreaming about solving the impossible question and winning approbation from all.

Albers, Donald J. and Constance Reid.   "An Interview of George B. Dantzig: The Father of Linear Programming."     College Mathematics Journal.   Volume 17, Number 4; 1986   (pp. 293-314).

Brunvand, Jan Harold.   Curses! Broiled Again!     New York: W. W. Norton, 1989.   ISBN 0-393-30711-5   (pp. 278-283).

Dantzig, George B.     "On the Non-Existence of Tests of 'Student's' Hypothesis Having Power Functions Independent of Sigma."     Annals of Mathematical Statistics .   No. 11; 1940   (pp. 186-192).

Dantzig, George B. and Abraham Wald.   "On the Fundamental Lemma of Neyman and Pearson."     Annals of Mathematical Statistics .   No. 22; 1951   (pp. 87-93).

Pearce, Jeremy.   "George B. Dantzig Dies at 90."     The New York Times .   23 May 2005.

By David Mikkelson

David Mikkelson founded the site now known as snopes.com back in 1994.

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Dantzig's unsolved homework problems

From Wikipedia:

An event in George Dantzig 's life became the origin of a famous story in 1939 while he was a graduate student at UC Berkeley. Near the beginning of a class for which Dantzig was late, professor Jerzy Neyman wrote two examples of famously unsolved statistics problems on the blackboard. When Dantzig arrived, he assumed that the two problems were a homework assignment and wrote them down. According to Dantzig, the problems "seemed to be a little harder than usual", but a few days later he handed in completed solutions for the two problems, still believing that they were an assignment that was overdue.

What were the two unsolved problems which Dantzig had solved?

J. M. ain't a mathematician's user avatar

2 Answers 2

I think the two problems appear in these papers:

Dantzig, George B. "On the Non-Existence of Tests of 'Student's' Hypothesis Having Power Functions Independent of Sigma." Annals of Mathematical Statistics. No. 11; 1940 (pp. 186-192).

Dantzig, George B. and Abraham Wald. "On the Fundamental Lemma of Neyman and Pearson." Annals of Mathematical Statistics. No. 22; 1951 (pp. 87-93).

Read more at http://www.snopes.com/college/homework/unsolvable.asp#6oJOtz9WKFQUHhbw.99

EDIT: In case snopes ever goes belly up, the story can be found in Albers, Reid, and Dantzig, An Interview with George B. Dantzig : The Father of Linear Programming, College Math J 17 (1986) 292-314. The interview has also been reprinted in Albers, Alexanderson, and Reid, More Mathematical People, page 67.

Gerry Myerson's user avatar

I'm a few years late to the party, but in fact the problem in the first, solo paper is easy to state with only elementary background, and the arguments in it are entirely reasonable for a talented young grad student to come up with. I have not taken the time to read the second paper. This topic comes up from time to time with interest from a very broad array of people, and nobody seems to have written a straightforward description of either problem, so I'll provide such a description for the first one.

For those with some background: Dantzig showed that in the situation of Student's t-test, the only way to get a hypothesis test whose power for any given alternative is independent of the standard deviation is to use a silly test which always has an equal probability of rejecting or failing to reject, which is obviously not useful.

In an unusual amount of detail, aimed at those with no statistical knowledge:

Lots of data is approximately normally distributed ("bell-shaped"), like IQ scores, birthweights, or people's height. The classical Central Limit Theorem gives one explanation for this phenomenon: complicated traits like birthweight can often be thought of as the result of adding up a large number of competing effects, like the presence or absence of specific genes. It is a statistical fact that under very general hypotheses, adding up many such effects tends to result in a normal distribution. For such data, you'll "usually" get the average value, and with enough observations, you can predict with high accuracy just how likely it is to get a certain amount above or below that average.

A century ago, William Gosset was Head Experimental Brewer at Guinness. He came up against something like the following problem. Certain strains of barley have approximately normally distributed yields. Using only a few data points, how could he tell which type of barley is better, and more importantly, how could he quantify his certainty that his conclusion wasn't simply due to random chance?

A little more formally, say our current strain of barley has an average yield of 100 units, and we're only interested in switching to the new strain if its yield is at least 105 units. So, we have two specific hypotheses:

At the end of the day, we're going to need to pick one strain of barley or the other. There are hence four probabilities of interest:

We want to somehow minimize the probability of the two types of mistakes, B and C, but doing so requires a trade-off.

Gosset developed a clever test where you can specify in advance the probability of making mistake B--often it's set at 5%. This is called the significance level of the test. Gosset published the it under the pseudonym "Student", and it is now called Student's t-test. One excellent thing about his procedure is that you don't need to know in advance how variable the yield actually is in the sense that the probability of mistake B is always your pre-set value.

If you use his procedure, you can also compute the probability of making mistake C. The power of the test is probability D (namely 1-C), which is thought of as the ability of the test to correctly tell us to switch to the new method. Unlike the significance level, the power of Gosset's procedure does depend on the true variability of the yield.

This dependence makes some intuitive sense, too. Suppose the new strain does have an average yield of 105 units. If that yield had almost no variation, you would expect it to be much easier to correctly switch to the new strain than if the yield had enormous variation which "muddies your data". Of course, expecting something and proving it are two different things! As mentioned above, in the world where the average yield is 100 units, the error probability of Student's t-test is independent of the variation of the yield, so there is certainly something interesting going on.

Here's where Dantzig came in. We could ask if there is any test whatsoever which has the property that, for every fixed alternative, the power does not depend on the true variability of the yield. Dantzig showed that, while such tests technically exist, they are uninteresting in that probabilities A, B, C, and D are all 50%.

Closing remarks:

Finally, I wanted to comment on the tendency towards hyperbole. In Dantzig's 1986 College Mathematics Journal interview , Dantzig is quoted as calling the problems "two famous unsolved problems in statistics". In Dantzig's obituary (repeated on Wikipedia currently), this turned into "two of the most famous unsolved problems in statistics". While this is not my field and I am not old, I'm extremely dubious about the "most famous" claim. For instance, there seems to have been no rush to publish the second solution (it waited for Dantzig's thesis and an accident of someone else solving it). MathSciNet has only 5 citations for the first paper, three historical, and 7 citations for the second, again three historical. These are not the citation counts I would expect from solutions to a field's "most famous unsolved problems", even accounting for recent citation bias.

These exaggerations are frankly not necessary. Dantzig's reputation is enormous already, and the true story of a talented young grad student cleverly finding a few pages of brilliant argument that had eluded his teacher---something he never would have looked for if he knew that what he was working on was unsolved---is enough.

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Student solves unsolvable problems, student solves ‘unsolvable’ statistical problems.

George Bernard Dantzig solves unsolvable problems

Mr. George Bernard Dantzig, a doctoral candidate at the University of California (USC), Berkeley in 1939, arrived late for his graduate-level statistics class and found two problems written upon the blackboard. Not knowing that they were examples of ‘unsolvable’ statistical problems, he mistook them for a homework assignment, jotted them down and solved them. The equations that he solved are actually more accurately described best as unproved statistical theorems, rather than unsolvable problems.

Assumed They’d Been Assigned For Homework

In 1986, George recalled the event in a College Mathematics Journal interview: “It happened because during my first year at Berkeley, I had arrived late one day for a Jerzy Neyman class. On the blackboard there were two problems. I assumed they’d been assigned for homework, so I copied them down. A few days later I apologized to Neyman for taking so long, but the problems seemed to be harder than usual. I asked him if he still wanted them. He said yes, and told me to put them on his desk.

All Excited

About six weeks later, around eight o’clock on a Sunday morning, we were woke by someone banging on our front door. It was Neyman. He rushed in with the papers in hand, all excited. “I’ve just written an introduction to one of your papers. Please read it so I can send it off right away for publication.” For a minute I had no idea what he was talking about. To make a long story short, the problems on the blackboard that I had solved, thinking that they were homework, were in fact two famous unsolvable math problems in statistics. That was the first inkling I had that there was anything special about them.”

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A Mathematician Who Solved Major Problems

The death of mathematician George Dantzig is a scientific watershed. Dantzig developed "linear programming" and the simplex method, used to solve complex efficiency problems for large organizations. Stanford professor Keith Devlin offers insight on Dantzig's influence.

SCOTT SIMON, host:

The US military and other gigantic enterprises rely on sophisticated computer programs to figure out what equipment and supplies they could, should or might need, from the number of troops and aircraft to the number of $700 toilet seats installed in submarines, the screws needed to hold them in place. The mathematics behind many of these computer programs is known as linear programming. It was developed by the US Air Force during the Second World War. The leading figure in its development was a young mathematician named George Dantzig. Last week Mr. Dantzig died at his home in Palo Alto, California. Our math guy Keith Devlin joins us to talk about his life.

Keith, thanks very much for being with us.

KEITH DEVLIN:

Hi, Scott; nice to be here.

SIMON: Well, help us understand linear programming, first.

DEVLIN: OK, so linear programming, it's a method for planning, for optimization. It's used if you want to allocate resources or production planning, worker scheduling, routing telephone calls. You can use it for planning investment portfolios, market planning, military strategies, any situation where you have an awful lot of variables that you need to juggle in order to optimize something to find the best solution. Linear programming--it's one of the most amazing pieces of genius in the history of mathematics. It takes ...(unintelligible)...

SIMON: Let me--to try and understand it first, let me interject, and I mean this sincerely.

DEVLIN: Yep. Sure.

SIMON: Is this--can you even begin to contemplate doing linear programming with a pencil and a piece of paper?

DEVLIN: You can do very simple examples of it where there are three or four parameters, but once you've got a large number of parameters, no, you can't use a pencil and paper. This is a product of the computer age. It's really no accident that these techniques were developed at the same time as modern computers were being developed. And, in fact, Dantzig himself not only developed this method, he introduced a method for solving the linear programming problem called the simplex method and turned that into a computer program, an algorithm, the simplex algorithm, which is--in fact, until computers started to be used for e-mail on the World Wide Web in the '80s and '90s, the single most important use of computers, the biggest user of computer time in the entire world was running the simplex algorithm to solve linear programming problems. I mean, no large organization cannot exist, or stay in business, without the simplex algorithm to solve linear programming problems.

One of the first applications was in the early post-war, early post-Second World War era, when food was scarce and there was an issue of what was the best diet for people. George Dantzig himself was able to map out a good minimal-priced optimal diet whereby people could be healthy, could grow up to be reasonably fit adults, with a minimal expenditure of money, and thereby, of course, the US government was able to direct farmers to grow various kind of crops, various kind of animals and livestock and so forth.

SIMON: There's a story about him, which I love, I read recently. I want to get you to tell it, about when he was a young student, handing in a test paper.

DEVLIN: Oh, yeah, this was when he first went out to--he got his master's degree by then and he went back to--this was in the late '30s--Berkeley to start to pursue a PhD, and he was enrolled in a statistics class and he went--he arrived late for class one day and on the back door there were a couple of problems written down and he thought `Oh, that must be the homework for tonight.' So he wrote the problems down and took them home and, you know, usually it took him an hour or two to solve the problem. This one--these two seemed hard, and it took him a few days, but he eventually got them out and he went along to the professor and apologized profusely for being late and said, you know, he just found these more difficult than usual. Six weeks later, early one Sunday morning, George was woken up by someone banging excitedly on the front door. So he went down, I guess in his pajamas, and there was the professor. And the professor was just overjoyed because he said, `You know, you've just solved two of the most difficult unsolved problems in statistics. We've got to write them up and submit them for publication right away.' And from that point on, it was clear that George Dantzig was no ordinary statistician.

SIMON: Our math guy, Keith Devlin, who's executive director for the Center for the Study of Language and Information(ph) at Stanford University and the author of "The Math Instinct: Why You're a Mathematical Genius"--Doesn't apply to all of us, I'm sure--along with "Lobsters, Birds, Cats and Dogs(ph)."

Keith, thanks very much.

DEVLIN: OK; my pleasure, Scott.

Copyright © 2005 NPR. All rights reserved. Visit our website terms of use and permissions pages at www.npr.org for further information.

NPR transcripts are created on a rush deadline by an NPR contractor. This text may not be in its final form and may be updated or revised in the future. Accuracy and availability may vary. The authoritative record of NPR’s programming is the audio record.

Remembering George Dantzig: The real Will Hunting

george dantzig solved 2 problems

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Good Will Hunting Scene Math Problem

If you’ve ever seen Good Will Hunting , you’re probably familiar with the setup. Matt Damon’s character, Will Hunting, is a young Bostonian with a quintessentially ’90s haircut working as a janitor at MIT. A professor at MIT has set up a challenging math problem on the blackboard outside his office; Will solves it anonymously and easily, even though he doesn’t attend the college.

The professor sets up another, even harder math problem. Again, Will solves it, although the professor catches him in the act this time, prompting Will to flee. When the professor asks his name, assuming the janitor is vandalizing his blackboard, Will responds “ Fahk you,” and disappears. The professor is shocked to discover that the janitor has written the correct answer on the blackboard.

It’s pure Hollywood, improbable and endearing. But this particular scene actually has its roots in reality, although the protagonist in this case didn’t have a strong jawline and huge forehead. Instead, it was based on an anecdote from the life of the legendary math student George Bernard Dantzig.

Luckily late to class

Dantzig, who died in 2005, was a celebrated mathematician best known for his development of the simplex algorithm , a popular algorithm used in linear programming to find the optimal solution to problems that can be described through linear mathematics; for example, how to maximize profits and minimize losses, or how to determine the best diet for an army at the least cost. But part of what made his work in this field feasible was being late for class one day when he was a student.

Dantzig was studying statistics under professor Jerzy Neyman at UC Berkley’s graduate program. In an interview, Dantzig explained that “I arrived late one day at one of Neyman’s classes. On the blackboard there were two problems that I assumed had been assigned for homework. I copied them down. A few days later I apologized to Neyman for taking so long to do the homework — the problems seemed to be a little harder than usual.”

Six weeks later, Neyman rushed over to Dantzig’s house and excitedly told him that he had just written an introduction to “one of your papers. Read it so I can send it out right away for publication.” Dantzig had no idea what Neyman was talking about until he explained that the two problems on the blackboards were famously unsolved statistical problems — not homework at all.

Later on in his graduate degree, Dantzig was struggling to come up with a thesis topic. Allegedly, when Dantzig told Neyman this, the professor shrugged it off and told him to put the two math problems in a binder for submission — they would count as his thesis.

How the story spread

Years later, Dantzig was an accomplished computer scientist and mathematician when his colleague Don Knuth stopped him. “Hey, George,” he said. “I was visiting in Indiana recently and heard a sermon about you in church. Do you know that you are an influence on Christians of middle America?”

As it turns out, Dantzig’s story had been making the rounds as an example of the power of positive thinking. Initially, a Lutheran televangelist in Los Angeles had picked up the story, albeit with major embellishments. The televangelist claimed that the problems had stumped even Einstein and that Dantzig’s professor hired him on the spot after seeing the correct solutions. Despite these exaggerations, the moral of the story remained the same. Because Dantzig didn’t know these problems were unsolved, he wasn’t constrained by what he would have believed to be his limits. As he later wrote about the chance event:

“If I had known that the problems were not homework but were in fact two famous unsolved problems in statistics, I probably would not have thought positively, would have become discouraged, and would never have solved them.”

As Dantzig’s story spread out among religious sermons, his identity became dropped from the story. Others began telling it as well with their own embellishments. The story became an urban legend related to the power of positive thinking, one that would eventually find new life in Good Will Hunting .

Silhouette of human hand with open palm praying to god at sunset background

Reality Decoded

Making clear what is hidden in plain sight, student arrives late for class and solves famous unsolved math problem because he thought it was just regular homework.

In 1939, George Dantzig arrived late for a graduate-level statistics class at the University of California, Berkeley. On the board were two problems of “unsolved” statistics that George mistook for a homework assignment. He copied them down and started working on them from home, six weeks later he turned in the work late, hoping to get at least some credit for the assignment but nothing great. Dantzig’s statistic professor later notified him of what he achieved and the impact it would have on the math community.

George Dantzig recounted his feat in a 1986 interview for the College Mathematics Journal:

It happened because during my first year at Berkeley I arrived late one day at one of [Jerzy] Neyman’s classes. On the blackboard there were two problems that I assumed had been assigned for homework. I copied them down. A few days later I apologized to Neyman for taking so long to do the homework — the problems seemed to be a little harder than usual. I asked him if he still wanted it. He told me to throw it on his desk. I did so reluctantly because his desk was covered with such a heap of papers that I feared my homework would be lost there forever. About six weeks later, one Sunday morning about eight o’clock, [my wife] Anne and I were awakened by someone banging on our front door. It was Neyman. He rushed in with papers in hand, all excited: “I’ve just written an introduction to one of your papers. Read it so I can send it out right away for publication.” For a minute I had no idea what he was talking about. To make a long story short, the problems on the blackboard that I had solved thinking they were homework were in fact two famous unsolved problems in statistics. That was the first inkling I had that there was anything special about them.

How many problems are waiting for us to solve them simply because we think we can’t?

Share this:, related articles, meet the man who can’t speak, talk, or walk and can only see within a foot who wrote 4 books and a screenplay.

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wow this is amazing. Sometimes greatness are discovered by mistake. 🙂

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TIL the mathematician George Dantzig solved two of the most famous problems of statistics, because he came into class too late to hear that they were supposed to be unsolvable

george dantzig solved 2 problems

His creation, the simplex method , is used everywhere. In traffic lights, and airline scheduling, and inside of cpus, by companies planning their production line-up. I mean everywhere. It is amazing that something so ubiquitous is completely invisible to pretty much everyone.

Well in fairness, the Simplex method is kinda... weird. Pivots and randomly making stuff negative... it's just weird.

Oh jeez. I just struggled with that last semester. Large load and linear algebra took a back seat.

Very similar to the discovery of Huffman encoding which is also used everywhere now: http://www.maa.org/press/periodicals/convergence/discovery-of-huffman-codes

Senior in industrial engineering here, we basically pray to Dantzig every morning before lecture

Wow, i had no idea those were unsolvable, and simplex is so easy to apply

Just like QNX.

I'm studying to be a programmer, so I feel like I should know this stuff, but I have no idea what it is and how it can be useful to me. Can you provide some examples?

airline scheduling

so you're saying it doesn't actually work worth a damn?

Relevant text, from Dantzig himself:

During my first year at Berkeley I arrived late one day to one of Neyman's classes. On the blackboard were two problems which I assumed had been assigned for homework. I copied them down. A few days later I apologized to Neyman for taking so long to do the homework - the problems seemed to be a little harder to do than usual. I asked him if he still wanted the work. He told me to throw it on his desk. I did so reluctantly because his desk was covered with such a heap of papers that I feared my homework would be lost there forever.
About six weeks later, one Sunday morning about eight o'clock, Anne and I were awakened by someone banging on our front door. It was Neyman. He rushed in with papers in hand, all excited: "I've just written an introduction to one of your papers. Read it so I can send it out right away for publication." For a minute I had no idea what he was talking about. To make a long story short, the problems on the blackboard which I had solved thinking they were homework were in fact two famous unsolved problems in statistics. That was the first inkling I had that there was anything special about them.

I wonder if it took Neyman six weeks to verify the solution or six weeks to find the homework in the heap of papers.

"Seemed to be a little harder than usual..."

I love that part

If this were written today with that kind of claim nonone would believe it and it would wind up on r/iamverysmart

Imagine how many currently unsolved problems would be solved if professors gave them to students for homework without telling them that no solution has been found yet? I'm sure the human psyche would tackle a problem differently if it already knows (or thinks) a solution exists rather than being uncertain if one exists.

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MAY 08, 2021

I n 1939, a first-year doctoral student at UC Berkeley named George Dantzig, arrived late to class. His professor, famous statistician Jerzy Neyman, had written two statistics problems on the blackboard. Dantzig quickly jotted them down, assuming that they were homework problems. A few days later, Dantzig turned in the problems late to Professor Neyman, apologizing for the overdue assignment. The problems had seemed “a little harder to do than usual.” Six weeks later, an ecstatic Professor Neyman knocked on Dantzig’s door. As it turns out, the problems weren’t homework at all. They just so happened to be two famous unsolved problems in statistics. And Dantzig had solved both of them. 

The story of the solved “homework problems” would later inspire an iconic scene in the 1997 academy award winning film, “Good Will Hunting,” centered on the story of Will Hunting, a 20-year-old South Bostonian janitor who is an unrecognized math prodigy. In the scene, a Massachusetts Institute of Technology math professor challenges his students to solve an incredibly difficult math problem, which he writes on a hallway blackboard. Will, who works as a janitor for MIT, comes across the problem and solves it in a matter of minutes. The next day, when the professor calls for the reveal of the mystery student who solved the problem, no one comes up. Puzzled, the professor puts up another challenging problem, which took two years to be solved. Once again, Will sees the new problem and solves it with ease, however this time, he gets caught. The rest of the movie’s trajectory follows a beautiful journey of Will learning to cope with the consequences of his traumatic childhood while exercising his intellectual capability. 

In the film, Will, played by a young Matt Damon, is portrayed as naturally intelligent; someone who is gifted with genius. Oftentimes, this is how intelligence is depicted by the media, as a trait that can only be genetically inheritable. Will was born a “genius.” Dantzig was “destined” to make mathematical breakthroughs. However, according to renowned psychologist and Stanford professor Carol Dweck, this view can actually be quite harmful. Curious to learn what differentiated kids who backed away from challenges and were quick to give up from kids who actively challenged themselves and persisted despite the obstacles, Dweck spent a good portion of her career studying the mindsets of children when facing tough challenges. Her study on the topic boiled the answer down to two distinctions: fixed mindset and growth mindset. 

Oftentimes, this is how intelligence is depicted by the media, as a trait that can only be genetically inheritable. Will was born a “genius.” Dantzig was “destined” to make mathematical breakthroughs.

A child who possesses a fixed mindset sees their abilities as set in stone. They are either good at math or not; either talented at singing or not. An individual’s skills are only of a certain amount, and if they face difficult challenges, their limited skills are put into question. In a TED Talk given by Dweck, she mentioned that with a fixed mindset, children are “gripped in the tyranny of now.” Her studies showed that students with fixed mindsets during tests showed more signs of cheating and comparing themselves with others who did worse than them. The children were desperate to salvage their “inherent” skills and talent. As Dweck puts it in her bestselling book, “Mindset,” “From the point of view of the fixed mindset, effort is only for people with deficiencies…. If you’re considered a genius, a talent, or a natural—then you have a lot to lose. Effort can reduce you.” In the person’s mind, they’ve lost the genetic lottery, and thereby, don’t have much incentive to try. It can seem pointless to make strong attempts at learning to play the cello when one is convinced that they don’t have any musical talent. Why try learning computer science when you know you’re bad with computers? This rigid and demeaning mindset hinders the expansion of our capabilities.

On the other hand, a growth mindset is believing that abilities can be developed through effort and persistence. A child who has a growth mindset is eager to undergo challenges to engage in new learnings. They are aware that they are capable of growth. As written in Dweck’s book, people with growth mindsets “believe a person’s true potential is unknown (and unknowable); that it’s impossible to foresee what can be accomplished with years of passion, toil, and training.” With this hope of greater potential, people become more willing to devote their time to improving their skills in various areas. During her study, Dweck conducted a CT scan on childrens’ brains and saw that the brains of kids with growth mindsets lit up with activity. When taking on new challenges and problems, the brain’s neurons make new and stronger connections, ultimately making the individual smarter. 

Growth mindset is present in people such as Dantzig. For starters, as a child, Dantzig was reared to constantly challenge himself and grow in his mathematical capabilities. His father, a mathematician, would challenge him with tough problems in projective geometry. In her study, Dweck emphasizes that the role of parents in fostering their childrens’ mindsets is crucial. Many of us may have grown up hearing consoling phrases from our parents such as, “It’s OK if you’re not good at science; not everyone is good at it.” It’s these types of words that shape our minds into thinking that we are not cut out for certain skills and that there’s no use in trying to gain them. Instead of fostering this restrictive attitude, Dweck proposes that parents “praise the effort that led to the outcome or learning progress.” Rather than praising intelligence or talent, parents should praise parts of the process such as effort, strategies and improvement. This allows children to see that their efforts are important and can lead to growth, thereby encouraging them to continue challenging themselves.

Many of us may have grown up hearing consoling phrases from our parents such as, “It’s OK if you’re not good at science; not everyone is good at it.”

Dantzig’s willingness to put such a high level of effort into a couple of homework problems shows that Dantzig genuinely cares about his learning and likes to challenge himself. Despite the immense difficulty of the problems, he persisted through the challenge and ultimately succeeded. Even when it went past the deadline, he still continued to work on them. He could have easily given up and not turned in the problems at all. However, he was willing to deal with the full challenges of the problems. 

However, if Dantzig had known that those two statistics problems weren’t homework problems, would he still have been able to solve them? Would he have even attempted to solve them in the first place? According to Dantzig himself, the answer is no: “If I had known that the problems were not homework but were in fact two famous unsolved problems in statistics, I probably would not have thought positively, would have become discouraged, and would never have solved them.” Therefore, Dantzig’s story may not be a complete display of a growth mindset after all. 

When Dantzig was working on the problems, he was under the illusion that they were for homework. This illusion tailored his mind to think that the problems “had to be solved” and that they were solvable by doctorate students such as himself. Here, we get the essence of a fixed mindset. As Dantzig said himself, he wouldn’t have been able to solve those problems if he knew that they were actually the famous unsolved statistics problems. Dantzig may not have even put in the effort to tackle the challenging problems simply because he was still a student and didn’t believe he had the capacity to do so. In this way, he would’ve limited himself. 

Dantzig’s case introduces a nuance to Dweck’s label of fixed and growth mindset: People can switch between both mindsets depending on the situation or the task. In other words, a person doesn’t always one-dimensionally either have a fixed mindset or a growth mindset. As Bill Gates, a strong advocate of Dweck’s work, writes in a review of her book, “My only criticism of the book is that Dweck slightly oversimplifies for her general audience. … most of us are not purely fixed-mindset people or growth-mindset people. We’re both. When I was reading the book, I realized that I have approached some things with a growth mindset (like bridge) while other things in a fixed mindset (like basketball).” With Dantzig’s case, we can see that one can engage in both fixed and growth mindsets depending on the situation. 

As Bill Gates, a strong advocate of Dweck’s work, writes in a review of her book, “My only criticism of the book is that Dweck slightly oversimplifies for her general audience. … most of us are not purely fixed-mindset people or growth-mindset people. We’re both.”

Thus, we can conclude that the first step to fostering a growth mindset is recognizing the areas in which we have fixed mindsets. It’s also crucial to be aware of how the media can trick our minds into becoming more and more fixed. Take “Good Will Hunting,” for instance. Will isn’t depicted as someone who worked exceedingly hard to be a math whiz. He reads lots of books, but everything else comes with that brain of his. Many films and news stories follow this portrayal: They glorify “special” individuals for their almost superhuman intellect, making consumers of the media feel all the more incapable. And the problem is only heightened by the fact that we crave stories such as this, stories of geniuses who solve crazy problems and save the world. We idolize people such as Albert Einstein and Stephen Hawking for their intelligence and oftentimes disregard their perseverance and hard work that contributed to their great success. This mindset isn’t fair to Einstein and Hawking, and it isn’t fair to ourselves. After all, we can be a lot more capable than we think. 

Back in 2008, Malcolm Gladwell wrote a piece in the New Yorker about how great discoveries are often “in the air,” and it doesn’t take a single genius to discover it. He uses an example about Alexander Graham Bell and his extraordinary invention of the telephone. He writes that while Bell is often credited for inventing the telephone, another man by the name of Elisha Gray had been working on the telephone around the same time. In fact, they filed notice with the U.S. Patent and Trademark Office in Washington, D.C. on the same day. Sadly, for Gray, only Bell became widely recognized as the inventor of the telephone. With this example, Gladwell makes the point that “Good ideas are out there for anyone with the wit and the will to find them.” He deconstructs the idea that solitary geniuses are destined to make certain discoveries. 

However, because our minds are so tailored to crediting a single genius, we become suspicious when others make similar breakthroughs. We instinctively question the credibility of their work. As Gladwell writes, “We’re reluctant to believe that great discoveries are in the air. We want to believe that great discoveries are in our heads—and to each party in the multiple the presence of the other party is invariably cause for suspicion.” However, if numerous other people are just as capable of making the same discoveries, then it must mean that our romantic notion of a “genius” is false. Near the end of his article, Gladwell refers to a notable essay written in the 1960s by sociologist Robert K. Merton. Merton writes, “A scientific genius is not a person who does what no one else can do; he or she is someone who does what it takes many others to do. The genius is not a unique source of insight; he is merely an efficient source of insight.” 

While there may be people out there in the world who have brains like Hunting, we shouldn’t rely on genetics for knowledge and talent. No one, not even the smartest individual, can get anywhere in life without the ability to face challenges and to persevere through them. In the words of Dweck, “The world of the future is going to be about taking on ill-defined, hard jobs that keep changing. It’s going to favor people who relish those challenges and know how to fix them.”A nd so that’s why we need to open and train our minds to remember:

George Dantzig put in a lot of effort to solve those problems, 

Will Hunting is Hollywood made

and that for every Alexander Bell, there’s an Elisha Gray. 

MAY 09, 2021

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george dantzig solved 2 problems

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PhD dissertations that solve an established open problem

I search for a big list of open problems which have been solved in a PhD thesis by the Author of the thesis (or with collaboration of her/his supervisor).

In my question I search for every possible open problem but I prefer (but not limited) to receive answers about those open problems which had been unsolved for at least (about) 25 years and before the appearance of the ultimate solution, there had been significant attentions and efforts for solving it. I mean that the problem was not a forgotten problem.

If the Gauss proof of the fundamental theorem of algebra did not had a gap, then his proof could be an important example of such dissertations.

I ask the moderators to consider this question as a wiki question.

23 Answers 23

I find George Dantzig's story particularly impressive and inspiring.

While he was a graduate student at UC Berkeley, near the beginning of a class for which Dantzig was late, professor Jerzy Neyman wrote two examples of famously unsolved statistics problems on the blackboard. When Dantzig arrived, he assumed that the two problems were a homework assignment and wrote them down. According to Dantzig, the problems "seemed to be a little harder than usual", but a few days later he handed in completed solutions for the two problems, still believing that they were an assignment that was overdue. Six weeks later, Dantzig received a visit from an excited professor Neyman, who was eager to tell him that the homework problems he had solved were two of the most famous unsolved problems in statistics. Neyman told Dantzig to wrap the two problems in a binder and he would accept them as a Ph.D. thesis.

The two problems that Dantzig solved were eventually published in: On the Non-Existence of Tests of "Student's" Hypothesis Having Power Functions Independent of σ (1940) and in On the Fundamental Lemma of Neyman and Pearson (1951).

I am quite surprised that nobody has mentioned Grothendieck's thesis. Apparently Laurent Schwartz gave Grothendieck a recent paper listing a number of open problems in functional analysis at one of their initial meetings. (Schwartz had just won the Fields at the time.) Grothendieck went away for a few weeks/ months and then returned with solutions to many (or all?) of the questions. In the course of the next few years Grothendieck became one of the world's leading functional analysts, before turning his attention to algebraic geometry.

This is the story I heard as part of mathematical gossip many, many years ago. Maybe someone who is more knowledgeable can chime in.

Another utterly spectacular thesis was Noam Elkies'. Among other things he settled a 200 year old problem posed by Euler!

Godel's Completeness Theorem, was part of his PHD thesis.

It was definitely an active field of research, but I don't know to what degree the problem was an open one, in the way we understand it today.

.. when Kurt Gödel joined the University of Vienna in 1924, he took up theoretical physics as his major. Sometime before this, he had read Goethe’s theory of colors and became interest in the subject. At the same time, he attended classes on mathematics and philosophy as well. Soon he came in contact with great mathematicians and in 1926, influenced by number theorist Philipp Furtwängler, he decided to change his subject and take up mathematics. Besides that, he was highly influenced by Karl Menger’s course in dimension theory and attended Heinrich Gomperz’s course in the history of philosophy. Also in 1926, he entered the Vienna Circle, a group of positivist philosophers formed around Moritz Schlick, and until 1928, attended their meetings regularly. After graduation, he started working for his doctoral degree under Hans Hahn. His dissertation was on the problem of completeness. In the summer of 1929, Gödel submitted his dissertation, titled ‘Über die Vollständigkeit des Logikkalküls’ (On the Completeness of the Calculus of Logic). Subsequently in February 1930, he received his doctorate in mathematics from the University of Vienna. Sometime now, he also became an Austrian citizen.

The thesis of Martin Hertweck answered the at that time 60-years-old isomorphism problem for integral group rings in the negative, by constructing a counterexample. That is, a pair of non-isomorphic finite groups $G$ and $H$ such that the group rings $\mathbb{Z}G$ and $\mathbb{Z}H$ are isomorphic. This result has been published afterwards in the Annals of Mathematics .

Scott Aaronson's thesis , Limits on Efficient Computation in the Physical World , refuted some popular wisdom .

In the first part of the thesis, I attack the common belief that quantum computing resembles classical exponential parallelism, by showing that quantum computers would face serious limitations on a wider range of problems than was previously known. In particular, any quantum algorithm that solves the collision problem -- that of deciding whether a sequence of $n$ integers is one-to-one or two-to-one -- must query the sequence $\Omega(n^{1/5})$ times. This resolves a question that was open for years; previously no lower bound better than constant was known. A corollary is that there is no "black-box" quantum algorithm to break cryptographic hash functions or solve the Graph Isomorphism problem in polynomial time.

There was even a second part to that thesis...

...Next I ask what happens to the quantum computing model if we take into account that the speed of light is finite -- and in particular, whether Grover's algorithm still yields a quadratic speedup for searching a database. Refuting a claim by Benioff, I show that the surprising answer is yes.

Lisa Piccirillo, who recently obtained her PhD from the University of Texas, Austin, showed that the Conway knot is not slice, answering a relatively famous open problem in topology. You can read a popular account of her work in Quanta here: https://www.quantamagazine.org/graduate-student-solves-decades-old-conway-knot-problem-20200519/ . Her paper proving this result was published in the Annals of Math; but I'm pretty sure it also constituted her dissertation (see https://gradschool.utexas.edu/news/studying-knots-and-four-dimensional-spaces ).

John von Neumann's dissertation seems to be an example with just the right timing.

But at the beginning of the 20th century [ in 1901, to be precise ], efforts to base mathematics on naive set theory suffered a setback due to Russell's paradox (on the set of all sets that do not belong to themselves). The problem of an adequate axiomatization of set theory was resolved implicitly about twenty years later by Ernst Zermelo and Abraham Fraenkel [i.e. there was active research on the question]. Zermelo–Fraenkel set theory provided a series of principles that allowed for the construction of the sets used in the everyday practice of mathematics, but they did not explicitly exclude the possibility of the existence of a set that belongs to itself. In his doctoral thesis of 1925, von Neumann demonstrated two techniques to exclude such sets—the axiom of foundation and the notion of class. The axiom of foundation proposed that every set can be constructed from the bottom up in an ordered succession of steps by way of the principles of Zermelo and Fraenkel. If one set belongs to another then the first must necessarily come before the second in the succession. This excludes the possibility of a set belonging to itself. To demonstrate that the addition of this new axiom to the others did not produce contradictions, von Neumann introduced a method of demonstration, called the method of inner models, which later became an essential instrument in set theory. The second approach to the problem of sets belonging to themselves took as its base the notion of class, and defines a set as a class which belongs to other classes, while a proper class is defined as a class which does not belong to other classes. Under the Zermelo–Fraenkel approach, the axioms impede the construction of a set of all sets which do not belong to themselves. In contrast, under the von Neumann approach, the class of all sets which do not belong to themselves can be constructed, but it is a proper class and not a set. Van Heijenoort, Jean (1967). From Frege to Gödel: a Source Book in Mathematical Logic, 1879–1931. Cambridge, Massachusetts: Harvard University Press. ISBN 978-0-674-32450-3. OCLC 523838.

A question, a book, and a couple of dissertations; the most relevant, I think, is the thesis by Petkovšek. Hopefully this is an acceptable MO answer. First, the question comes from Knuth in The Art of Computer Programming :

[50] Develop computer programs for simplifying sums that involve binomial coefficients. Exercise 1.2.6.63 in The Art of Computer Programming, Volume 1: Fundamental Algorithms by Donald E. Knuth, Addison Wesley, Reading, Massachusetts, 1968.

(For those unfamiliar, there is a pseudo log scale to rate each problem such that [50], as above, is the most difficult exercise, expected to take some years to answer).

A solution to this exercise is given by the book A = B by Marko Petkovsek, Herbert Wilf, and Doron Zeilberger (fully availble from the linked page).

On page 29 of the book, the authors mention a Ph.D. dissertation, and one of the author's Ph.D. thesis (among a few other works) that provide the main content of the book:

[Fase45] is the Ph.D. dissertation of Sister Mary Celine Fasenmyer, in 1945. It showed how recurrences for certain polynomial sequences could be found algorithmically. (See Chapter 4.) ... [Petk91] is the Ph.D. thesis of Marko Petkovšek, in 1991. In it he discovered the algorithm for deciding if a given recurrence with polynomial coefficients has a "simple" solution, which, together with the algorithms above, enables the automated discovery of the simple evaluation of a given definite sum, if one exists, or a proof of nonexistence, if none exists (see Chapter 8). A definite hypergeometric sum is one of the form $f(n) = \sum^{\infty}_{k=-\infty} F(n, k)$, where $F$ is hypergeometric.

Sources are

[Fase45] Fasenmyer, Sister Mary Celine, Some generalized hypergeometric polynomials , Ph.D. dissertation, University of Michigan, November, 1945.

[Petk91] Petkovšek, M., Finding closed-form solutions of difference equations by symbolic methods , Ph.D. thesis, Carnegie-Mellon University, CMU-CS-91-103, 1991.

June Huh's recent proof of Rota's conjecture (stated by Read in 1968 for graphical matroids and Rota in 1971 for all matroids) formed his 2014 Ph. D. thesis . For matroids over $\mathbb{C}$ , this appeared first in Huh's 2010 preprint ; for matroids over any field, this appeared in his work with Eric Katz (2011) ; for arbitrary matroids, see Adiprasito, Huh and Katz (2015) . As the dates would suggest, the thesis covers matroids over any field, but not the result on general matroids.

Vladimir Arnold's thesis was about his solution to Hilbert's 13th problem, which he had done a few years earlier. This info is missing from Wikipedia but some details are in Mactutor: https://mathshistory.st-andrews.ac.uk/Biographies/Arnold/

Does Serre's (Jean-Pierre) thesis qualifies ? He computed there a lot of homotopy groups of spheres. But I don't know how old was this problem in 1951.

Stephen Bigelow showed that braid groups are linear in his thesis at Berkeley in 2000 (the paper had already appeared in 1999 in JAMS, but he included it in his thesis).

John Thompson's thesis solved the famous and long-standing conjecture that a Frobenius kernel is nilpotent in the late 1950s. Not only was this noteworthy enough to be reported in the New York Times, but many of the techniques developed in the thesis played a major role in shaping finite group theory for decades to come.

Richard Laver's dissertation proved a long-standing conjecture of Fraïssé, that the scattered order types are well-quasi-ordered. But maybe that was not quite 25 years old at the time.

A very recent example is Eric Larson's 2018 dissertation The maximal rank conjecture [Lar1], which proves the following old conjecture:

Conjecture. (Maximal rank conjecture) Let $C \subseteq \mathbb P^r$ be a general Brill-Noether¹ curve. Then the restriction map $$H^0(\mathbb P^r, \mathcal O_{\mathbb P^r}(k)) \to H^0(C, \mathcal O_C(k))$$ has maximal rank, i.e. is injective if $h^0(\mathbb P^r, \mathcal O(k)) \leq h^0(C, \mathcal O(k))$ and surjective otherwise.

Historical remarks. Although I have been unable to find a definite place where this conjecture was stated, it is attributed to M. Noether by Arbarello and Ciliberto [AC83, p. 4]. Cases of the problem have been studied by M. Noether [Noe82, §8], Castelnuovo [Cas93, §7], and Severi [Sev15, §10].

In modern days, the conjecture had regained attention by 1982 [Har82, p. 79]. Partial results from around that time are mentioned in the introduction to [Lar2].

Larson's work culminates a lot of activity, including many papers by himself with other authors. An overview of the proof and how the different papers fit together is given in [Lar3].

References.

[AC83] E. Arbarello and C. Ciliberto, Adjoint hypersurfaces to curves in $\mathbb P^n$ following Petri . In: Commutative algebra (Trento, 1981) . Lect. Notes Pure Appl. Math. 84 (1983), p. 1-21. ZBL0516.14024 .

[Cas93] G. Castelnuovo, Sui multipli di una serie lineare di gruppi di punti appartenente ad una curva algebrica . Palermo Rend. VII (1893), p. 89-110. ZBL25.1035.02 .

[Har82] J. D. Harris, Curves in projective space . Séminaire de mathématiques supérieures, 85 (1982). Les Presses de l’Université de Montréal. ZBL0511.14014 .

[Lar1] E. K. Larson, The maximal rank conjecture . PhD dissertation, 2018.

[Lar2] E. K. Larson, The maximal rank conjecture . Preprint, arXiv:1711.04906 .

[Lar3] E. K. Larson, Degenerations of Curves in Projective Space and the Maximal Rank Conjecture . Preprint, arXiv:1809.05980 .

[Noe82] M. Nöther, Zur Grundlegung der Theorie der algebraischen Raumcurven . Abh. d. K. Akad. d. Wissensch. Berlin (1882). ZBL15.0684.01 .

[Sev15] F. Severi, Sulla classificazione delle curve algebriche e sul teorema d’esistenza di Riemann . Rom. Acc. L. Rend. 24 .5 (1915), p. 877-888, 1011-1020. ZBL45.1375.02 .

¹ Brill-Noether curves form a suitable component of the Kontsevich moduli space $\overline M_g(\mathbb P^r, d)$ of stable maps $\phi \colon C \to \mathbb P^r$ from a genus $g$ curve whose image has degree $d$ .

Does Scholze's PhD thesis count, as if I remember rightly he applied perfectoid spaces to prove some important special case of Deligne's weight-monodromy conjecture? (Not an expert, correct me if I'm wrong).

Also I believe Mirzakhani gave a proof of the Witten conjecture when she was still a student, so I don't know if that proof is incorporated into her PhD thesis. Coincidentally, Kontsevich's proof of the Witten conjecture was also given in his PhD dissertation and then published as a journal article Intersection Theory on the Moduli Space of Curves and the Matrix Airy Function .

Edit: I have not read Mirzakhani's thesis, but it does indeed seem to be the case that she gave a new proof of Witten's conjecture in that thesis. Taken from the following article :

In 2002 I received a rather apologetic letter from Maryam, then a student at Harvard, together with a rough draft thesis and a request for comments. After reading only a few pages, I was transfixed. Starting with a formula discovered by Greg McShane in his 1991 Warwick PhD, she had developed some amazingly original and beautiful machinery which culminated in a completely new proof of Witten’s conjecture, a relation between integrable systems of Hamiltonian PDEs and the geometry of certain families of 2D topological field theories.

A recent and rather spectacular quasi-example is the Ph.D. thesis of María Pe (advisor Javier Fernández de Bobadilla), entitled “On the Nash Problem for Quotient Surface Singularities” (2011).

While it was not was the full solution of the Nash problem (dated back to 1968) it included great steps towards the full solution, eventually presented by María and Javier themselves in Annals of Math. in 2012.

Peter Weinberger's Ph.D. thesis is a superb example:

    Proof of a Conjecture of Gauss on Class Number Two

See: https://en.wikipedia.org/wiki/Peter_J._Weinberger

Since the OP mentions Gauss, this entry could be an appropriate addition to the list:

Manjul Bhargava's PhD thesis, Higher composition laws (2001), concerns a problem going back to Gauss. In the nineteenth century, Gauss had discovered a fundamental composition law for binary quadratic forms which are homogeneous polynomial functions of degree two in two variables. No formula or law of the Gauss type was known for cubic or higher degree forms. Bhargava broke the impasse of 200 years by producing a composition law for cubic and higher degree forms.

What makes Bhargava’s work especially remarkable is that he was able to explain all his revolutionary ideas using only elementary mathematics. In commenting on Bhargava’s results Andrew Wiles, his Ph.D. advisor said “He did it in a way that Gauss himself could have understood and appreciated.”

[ source1 and source2 ]

I found this interview from 2014, after Bhargava won the Fields medal, an inspiring read: "Somehow, he extracts ideas that are completely new or are retwisted in a way that changes everything. But it all feels very natural and unforced — it’s as if he found the right way to think.”

A bit surprised that Leslie F. Greengard has not been mentioned. His PhD thesis from Yale was supervised by Vladimir Rokhlin, and is often cited for the development of the fast multipole method (FMM) which reduces the computation of the electrostatic or gravitational potential field/force for an N-particle system from $O(N^2)$ to $O(N)$ . Together with FFT, the Monte Carlo method, the simplex method for linear programming, Quicksort, QR algorithm, etc., FMM is regarded as one of the top 10 algorithms of the 20th century . To quote from the link,

This algorithm overcomes one of the biggest headaches of $N$ -body simulations: the fact that accurate calculations of the motions of $N$ particles interacting via gravitational or electrostatic forces (think stars in a galaxy, or atoms in a protein) would seem to require $O(N^2)$ computations—one for each pair of particles. The fast multipole algorithm gets by with $O(N)$ computations. It does so by using multipole expansions (net charge or mass, dipole moment, quadrupole, and so forth) to approximate the effects of a distant group of particles on a local group. A hierarchical decomposition of space is used to define ever-larger groups as distances increase. One of the distinct advantages of the fast multipole algorithm is that it comes equipped with rigorous error estimates, a feature that many methods lack.

Bart Plumstead's 1979 thesis at Chicago included (among other things) proofs of all three of the Eisenbud-Evans conjectures, which were widely considered to be among the most significant open questions in commutative algebra at the time.

Maria Chudnovsky's PhD thesis gives a proof of the Strong Perfect Graph Conjecture . This is a conjecture of Claude Berge from 1961 (hence meets the 25 year criterion), and was considered one of the hardest and most important open problems in graph theory at the time. It is joint work with Neil Robertson, Paul Seymour, and Robin Thomas and was published in the Annals in 2006. See The strong perfect graph theorem .

I suppose my own thesis fulfils this. Let $M$ be a monoid generated by a finite set $A$ and defined by a single defining relation $w=1$ . The language of all words $v \in A^\ast$ representing the identity element in $M$ is called the congruential language for $M$ . If the congruential language for $M$ is a context-free language, then Louxin Zhang [1] proved that the group of all units (two-sided invertible elements) $U(M)$ of $M$ is a virtually free group.

Zhang then asked in 1992 (conference held in 1990) the following questions:

In Chapter 3 of my thesis, I give an affirmative answer to both questions. In fact, I generalise the answer to Question 1 to to all monoids defined by arbitrarily many relations of the form $w_i = 1$ .

My thesis can be found on my website .

[1] Zhang, Louxin , Congruential languages specified by special string-rewriting systems, Ito, Masami (ed.), Words, languages and combinatorics, Kyoto, Japan, August 28–31, 1990. Singapore: World Scientific. 551-563 (1992). ZBL0875.68589 .

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USC Digital Folklore Archives

A database of folklore performances, student inadvertently solves never-before-solved math problems.

My informant told me about a story she heard about a student waking up late and rushing to their final, then frantically trying to finish the three equations on the board. The first two weren’t so bad, but the third was difficult. He finally finished and turned it into the professor only to find out later the third was actually not part of the test. Instead, it was a problem that had as of yet been unsolved. He had figured it out, though. My informant likes it because she thinks it would be cool to accidentally become famous like that and because it relates to one of her favorite movies, Good Will Hunting, since the main character in it easily solves equations no else could.

I like how the story reflects how we believe what we hear; when we are told something is impossible, it will seem much harder in our mind. But when we think something is supposed to be solvable, it may be easier to figure out, even if it’s never been done before. Limitations we place on ourselves are often illusory.

I looked into the story and found that it is actually based in truth. In 1939, George Dantzig arrived late to his graduate statistics class and saw two problems on the board, not knowing they were examples of problems that had never been solved. He thought they were a homework assignment and was able to solve them. He found out the reality six weeks later when his teacher let him know and helped him publish a paper about one of the problems.

Annotation: Cottle, Richard, Ellis Johnson, and Roger Wets. “George B. Dantzig.” Notices of the AMS 54.3 (2007). Web. April 23 2012.

 MacTutor

George bernard dantzig.

... gave me thousands of geometry problems while I was still in high school. ... the mental exercise required to solve them was the great gift from my father. The solving of thousands of problems during my high school days - at the time when my brain was growing - did more than anything else to develop my analytic power.
As a teenager, I prepared some of the figures that appeared in the book.
Since its first appearance nearly half a century ago the book has gone through a number of printings and has deservedly maintained its popularity.
During my first year at Berkeley I arrived late one day to one of Neyman 's classes. On the blackboard were two problems which I assumed had been assigned for homework. I copied them down. A few days later I apologized to Neyman for taking so long to do the homework - the problems seemed to be a little harder to do than usual. I asked him if he still wanted the work. He told me to throw it on his desk. I did so reluctantly because his desk was covered with such a heap of papers that I feared my homework would be lost there forever. About six weeks later, one Sunday morning about eight o'clock, Anne and I were awakened by someone banging on our front door. It was Neyman . He rushed in with papers in hand, all excited: "I've just written an introduction to one of your papers. Read it so I can send it out right away for publication." For a minute I had no idea what he was talking about. To make a long story short, the problems on the blackboard which I had solved thinking they were homework were in fact two famous unsolved problems in statistics. That was the first inkling I had that there was anything special about them.
My office collected data about sorties flown, bombs dropped, aircraft lost... I also helped other divisions of the Air Staff prepare plans called "programs". ... everything was planned in greatest detail: all the nuts and bolts, the procurement of airplanes, the detailed manufacture of everything. There were hundreds of thousands of different kinds of material goods and perhaps fifty thousand specialties of people. My office collected data about the air combat such as the number of sorties flown, the tons of bombs dropped, attrition rates. I also became a skilled expert on doing planning by hand techniques.
Berkeley made me an offer, but I didn't like it because it was too small. Or, to be more exact, my wife did not like it. It was a grand salary of fourteen hundred dollars a year. She did not see how we could live on that with our child David.
One of the first applications of the simplex algorithm was to the determination of an adequate diet that was of least cost. In the fall of 1947 , Jack Laderman of the Mathematical Tables Project of the National Bureau of Standards undertook, as a test of the newly proposed simplex method, the first large-scale computation in this field. It was a system with nine equations in seventy-seven unknowns. Using hand-operated desk calculators, approximately 120 man-days were required to obtain a solution. ... The particular problem solved was one which had been studied earlier by George Stigler ( who later became a Nobel Laureate ) who proposed a solution based on the substitution of certain foods by others which gave more nutrition per dollar. He then examined a "handful" of the possible 510 ways to combine the selected foods. He did not claim the solution to be the cheapest but gave his reasons for believing that the cost per annum could not be reduced by more than a few dollars. Indeed, it turned out that Stigler's solution ( expressed in 1945 dollars ) was only 24 cents higher than the true minimum per year $ 39 . 69 .
Linear programming is viewed as a revolutionary development giving man the ability to state general objectives and to find, by means of the simplex method, optimal policy decisions for a broad class of practical decision problems of great complexity. In the real world, planning tends to be ad hoc because of the many special-interest groups with their multiple objectives.
The tremendous power of the simplex method is a constant surprise to me.
If one would take statistics about which mathematical problem is using up most of the computer time in the world, then ... the answer would probably be linear programming.
[ Linear programming ] is used to allocate resources, plan production, schedule workers, plan investment portfolios and formulate marketing ( and military ) strategies. The versatility and economic impact of linear programming in today's industrial world is truly awesome.
Mathematical programming has been blessed by the involvement of at least two exceptionally creative geniuses: George Dantzig and Leonid Kantorovich .
The systematic development of practical computing methods for linear programming began in 1952 at the Rand Corporation in Santa Monica, under the direction of George B Dantzig. The author worked intensively on this project there until late 1956 , by which time great progress had been made on first-generation computers.
An impressive book, the work is very complete, its scientific level high, and its reading pleasant.
... it is interesting to note that the original problem that started my research is still outstanding - namely the problem of planning or scheduling dynamically over time, particularly planning dynamically under uncertainty. If such a problem could be successfully solved it could eventually through better planning contribute to the well-being and stability of the world.
For inventing linear programming and discovering methods that led to wide-scale scientific and technical applications to important problems in logistics, scheduling, and network optimization, and to the use of computers in making efficient use of the mathematical theory.
In recognition of his outstanding contribution to engineering and the sciences through his pioneering work in mathematical programming and his development of the simplex method. His work permits the solution of many previously intractable problems and has made linear programming into one of the most frequently used techniques of modern applied mathematics.
A member of the National Academy of Engineering, the National Academy of Science , the American Academy of Arts and Sciences and recipient of the National Medal of Science, plus eight honorary degrees, Professor Dantzig's seminal work has laid the foundation for much of the field of systems engineering and is widely used in network design and component design in computer, mechanical, and electrical engineering.

References ( show )

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Honours awarded to George Dantzig

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Malevus

George Dantzig: The Story of The Overlooked Genius

The unappreciated talent. George Dantzig “accidentally” solved two unresolved statistics questions. But he was passed up for the Nobel Prize.

George Dantzig

Who is George Dantzig?

The simplex method was created by American mathematician and computer scientist George Dantzig, who is well recognized for his work in linear programming. He was born on November 8th, 1914, in Portland, Oregon, and graduated from the University of California, Berkeley, with a Ph.D. in mathematics in 1946.

What did George Dantzig develop?

The simplex algorithm, which Dantzig developed, is often used to solve linear programming problems. The goal of linear programming is to maximize a linear objective function within a set of linear constraints using a mathematical approach called linear programming.

What is the famous “homework” story of George Dantzig?

While a PhD student at Berkeley, Dantzig was the subject of a famous anecdote. After being late to a statistics lesson, Dantzig thought he saw homework written on the chalkboard, so he answered the questions and turned them in. After Dantzig’s answers were published in a prestigious publication, the two hitherto unsolved problems in statistics were dubbed the “homework problems” by statisticians everywhere.

What movie is related to George Dantzig?

A memorable scene from Good Will Hunting has Matt Damon’s character, a janitor at a university, tackling an almost impossible graph problem on a chalkboard. Certain details were changed for dramatic effect, but the basic tale is based on real events related to George Dantzig.

In 1939, George Bernard Dantzig, a doctorate candidate at the University of California, Berkeley, arrived a few minutes late to Jerzy Neyman’s statistics lecture while there were two homework problems posted on the board. He wrote them down and spent many days figuring out the answers. He was unaware that these were really two well-known statistics theorems that had never been proved before, not just regular exercise problems.

Dantzig subsequently said in an interview that:

A few days later I apologized to Neyman for taking so long to do the homework—the problems seemed to be a little harder than usual. I asked him if he still wanted it. He told me to throw it on his desk. I did so reluctantly because his desk was covered with such a heap of papers that I feared my homework would be lost there forever. About six weeks later, one Sunday morning about eight o’clock, we were awakened by someone banging on our front door. It was Neyman. He rushed in with papers in hand, all excited: “I’ve just written an introduction to one of your papers. Read it so I can send it out right away for publication.” For a minute I had no idea what he was talking about. To make a long story short, the problems on the blackboard that I had solved thinking they were homework were in fact two famous unsolved problems in statistics.

The most renowned statistician in the world at the time, Neyman, was then asked by Dantzig the next year what subject he should choose for his doctoral thesis. Neyman shrugged and said, “Just put your treatments of the two issues in a folder.” He would accept it as a doctoral thesis.

Dantzig’s early life

George Dantzig

The eldest child of Tobias Dantzig and Anja Ourisson, George Bernard Dantzig was born in Portland, Oregon. The parents had met while attending Henri Poincaré lectures at the Sorbonne in Paris, where they were both students.

They moved to the United States after getting married, where Tobias Dantzig, a native of Lithuania, had to start out by working odd jobs like a road builder and a lumberjack due to language barriers before obtaining a Ph.D. in mathematics from Indiana University; his wife took the master’s degree in French.

The parents thought that their children would have better chances in life if they were given the first names of famous people. Thus, the younger boy was given the first name Henri (after Henri Poincaré) in the hopes that he would one day become a mathematician, while the elder son was given the name George Bernard in the hopes that he would one day become a writer (like George Bernard Shaw).

The father taught mathematics at different institutions, including Johns Hopkins (Baltimore, Maryland), Columbia University (New York), and the University of Maryland, while the mother worked at the Library of Congress in Washington, DC. A book he released in 1930 on the history of the evolution of mathematics, Number – The Language of Science , has been reissued several times (most recently in 2005).

Dantzig continued to struggle with arithmetic in the early grades, but owing to his father’s daily assignment training regimen, particularly in geometry, Dantzig finally received top marks.

George Dantzig started his mathematical studies at the University of Maryland because, despite the fact that both of his parents were employed, the family did not have enough money to finance his studies in physics and mathematics at a prestigious university.

George Dantzig moved to the University of Michigan after receiving a bachelor’s degree, where he went on to complete his graduate studies in 1937. He subsequently accepted a position at the U.S. Bureau of Labor Statistics and participated in research on urban consumers’ purchasing habits after becoming weary of abstract mathematics.

Dantzig was a heartfelt statistician

Dantzig first became interested in statistical concerns and techniques while working in this position. He requested Jerzy Neyman’s permission in 1939 to attend his PhD studies at the University of California, Berkeley (with a “teaching assistantship”). And thus, one day, the event that was described above occurred.

The PhD program was still in progress when the United States joined World War II . Dantzig relocated to Washington, D.C., and accepted a post as the director of the Statistical Control Division at the headquarters of the U.S. Air Force. He discovered that the military’s knowledge of the real inventory of aircraft and equipment was insufficient.

He devised a method to collect the necessary data in detail, particularly to make a thorough contract award, including the need for nuts and bolts.

Dantzig briefly returned to Berkeley after the war, where he eventually received his degree. Not simply for financial reasons, but also because he preferred the chances and challenges of working for the Air Force, he declined an offer from the university to continue working there.

Dantzig saw the need to dynamize this rather static model and was motivated by the input-output analysis approach of the Russian-American mathematician Wassily Leontief, who had a position at Harvard University in Cambridge starting in 1931. Additionally, he aimed to improve it to the point where hundreds or even thousands of actions and locations could be recorded and optimized; at the time, this was a fascinating computing hurdle.

Dantzig’s advancements in military planning

George Dantzig, Anne Dantzig, and President Gerald Ford (National Medal of Honor ceremony, 1971).

While employed by the Pentagon, Dantzig came to the conclusion that many planning choices were based solely on experience rather than objective criteria, yielding less than ideal outcomes. Linear inequalities are often used to characterize the requirements (restrictions), and specifying an objective function establishes the purpose of optimization, such as maximizing profit or decreasing resource consumption.

In English, the planning technique created by Dantzig is known as “linear programming,” where “ programming ” refers not to programming in the modern meaning of the word but rather to the phrase used in the military for the planning of procedures. The selected linear function modeling is referred to as being “linear.”

A half-plane in two dimensions and a half-space in three dimensions are both defined by a linear inequality. Convex polygons or convex polyhedrons are produced when many inequalities are taken into account; in the n-dimensional case, the corresponding convex structure is known as a “simplex”.

The so-called Simplex Algorithm , which Dantzig created in 1947, is a systematic approach for computing the best answer. Dantzig himself said of it: “The tremendous power of the simplex method is a constant surprise to me.”

The creation of the simplex algorithm, a technique for resolving linear programming problems, is widely regarded as one of Dantzig’s most important accomplishments. The goal of linear programming is to maximize a linear objective function within a set of linear constraints using a mathematical approach. The simplex algorithm has had a significant influence on many fields, including business, economics, and engineering, as a tool for tackling problems in linear programming.

His work on duality in linear programming is a cornerstone of modern optimization theory. To describe the association between a dependent variable and one or more independent variables, he also made significant contributions to the statistical procedure known as linear regression. George Dantzig is called “the father of linear programming” for that.

He wasn’t seen worth of Nobel Prize

George Dantzig close-up colored portrait photograph

When Dantzig visited Princeton University to speak with John von Neumann towards the end of the year, the algorithm saw its first refinement. This bright mathematician and computer scientist quickly saw similarities between the methods he and Oskar Morgenstern outlined in their newly released book, “The Theory of Games,” (1944) and the linear optimization approach.

The search techniques have significantly improved over time, notably with the advent of computer use. Although other strategies, such as nonlinear modeling, were also studied, Dantzig’s “linear programming” technique was finally proven to be adequate.

Tjalling C. Koopmans, professor of research in economics at the University of Chicago, realized the value of linear planning from an economic perspective after speaking with Dantzig. His famous theory on the optimal use of exhaustible resources was born out of this. To the surprise of everyone in the field, Dantzig was left unaccomplished when Koopmans received the Nobel Prize in Economics in 1975, together with the Russian mathematician Leonid Vitaliyevich Kantorovich, who had earlier proposed comparable methods in 1939. But it took the West two decades to learn about them. Dantzig, who was always kind to his fellow men, handled this with remarkable perseverance, demonstrating his high degree of expertise.

Dantzig went to the RAND Corporation in Santa Monica in 1952 to continue developing computerized execution of processes after his work with the Air Force. He established the Operations Research Center after accepting a post at Berkeley’s Department of Industrial Engineering in 1960.

When it was first published in 1963 by Princeton University Press, his book Linear Programming and Extensions established the field of linear optimization. Dantzig began working at Stanford in 1966, when he also established the Systems Optimization Lab (SOL). He oversaw a total of 41 PhD students over the course of more than 30 years, all of them had bright futures in academia and the workplace after receiving their degrees from Dantzig.

Dantzig has received multiple honorary degrees and memberships in academies in recognition of his significant scientific accomplishments, including the National Medal of Science and the John von Neumann Theory Prize. The George B. Dantzig Prize is given every three years by the Mathematical Optimization Society (MOS) and the Society for Industrial and Applied Mathematics (SIAM) in recognition of the scientist and his achievements.

His health quickly deteriorated shortly after a celebration of his 90th birthday in 2004; a diabetes condition mixed with cardiovascular issues ultimately caused his death.

The two unsolved homework problems that George Dantzig solved

The doctoral student George Bernard Dantzig came late to Jerzy Neyman’s statistics lecture in 1939, when two homework assignments were already written on the board. He put them in writing and spent many days trying to solve them. To him, these seemed like ordinary math exercises, but upon further investigation, he discovered that they were, in fact, proofs of two well-known theorems in statistics that had never been proven previously.

1. “On the Non-Existence of Tests of “Student’s” Hypothesis Having Power Functions Independent of σ”, 1940

In the paper, Dantzig investigates whether or not the power function (i.e., the likelihood of rejecting the null hypothesis) of the statistical test for the “Student’s” hypothesis (commonly known as the t-test) can be designed to be independent of the standard deviation of the population (σ).

The “Student’s” hypothesis is a statistical hypothesis test used to evaluate whether the means of two populations are substantially different from each other; it was named after the statistician William Sealy Gosset, who wrote under the pseudonym “Student.” A common statistical procedure for comparing the means of two samples, the t-test is based on the “Student’s” hypothesis and has extensive use.

Dantzig demonstrated that a power function independent of σ cannot be designed for a statistical test of the “Student’s” hypothesis. He then explained his results and gave evidence for them. The study has received several citations because of its significance for the development of statistical theory.

2. “On the Fundamental Lemma of Neyman and Pearson”, 1951

In 1951, George Dantzig published an article in the Annals of Mathematical Statistics titled “On the Fundamental Lemma of Neyman and Pearson.” As a result of statistical theory, Neyman and Pearson’s fundamental lemma has to do with the power of statistical tests, which Dantzig proves in his article.

Neyman and Pearson’s “fundamental lemma” is a universal conclusion that establishes a connection between the null and alternative hypotheses in a statistical test. If the null hypothesis holds, then the likelihood of detecting a test statistic that is more extreme than a specified value (the critical value) is proportional to the sample size of the test. If the null hypothesis is correct, then the test’s power (the probability of rejecting the null) will be proportional to the test size.

Dantzig provides a demonstration of Neyman and Pearson’s fundamental lemma and examines how this finding has practical consequences for statistical testing in his work. The study again has received a lot of attention for its groundbreaking addition to statistical theory.

George Dantzig, the real Good Will Hunting

A scene from the Good Will Hunting movie with a character inspired from George Dantzig,

The American drama film “Good Will Hunting,” starring Matt Damon and Robin Williams, was directed by Gus Van Sant and released in 1997. Will Hunting, a young guy from South Boston who is a math prodigy yet works as a janitor at MIT, is the protagonist of this film. An MIT professor sees potential in Will, encourages him to pursue mathematics, and ultimately helps him conquer his own personal issues.

A memorable scene from Good Will Hunting has Matt Damon’s character, a janitor at a university, tackling an almost impossible graph problem on a chalkboard. Certain details were changed for dramatic effect, but the basic tale is based on real events related to George Dantzig. One day, future renowned mathematician George Dantzig was running late to his statistics class when he saw two statistical questions written on the whiteboard and assumed they were homework assignments. Dantzig later casually solved the long-unsolved problems of statistics.

George Dantzig’s discoveries and contributions

George Dantzig made important contributions to operations research and mathematical modeling. These are the important discoveries and contributions he made that bear mentioning:

When taken as a whole, Dantzig’s contributions to the fields of mathematics and computer science were influential and shaped the manner in which modern corporations and organizations use mathematical modeling to address difficult issues.

Bibliography

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By Bertie Atkinson

Bertie Atkinson is a history writer at Malevus. He writes about diverse subjects in history, from ancient civilizations to world wars. In his free time, he enjoys reading, watching Netflix, and playing chess.

Encyclopedia Britannica

Norbert Wiener.

George Dantzig

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George Dantzig , (born Nov. 8, 1914, Portland , Ore., U.S.—died May 13, 2005, Stanford, Calif.), American mathematician who devised the simplex method , an algorithm for solving problems that involve numerous conditions and variables, and in the process founded the field of linear programming .

Dantzig earned a bachelor’s degree in mathematics and physics from the University of Maryland (1936) and a master’s degree in mathematics from the University of Michigan (1937) before joining the U.S. Bureau of Labor Statistics as a statistician. In 1939 he entered the graduate mathematics program at the University of California , Berkeley. From 1941 to 1946 Dantzig was the civilian head of the Combat Analysis Branch of the U.S. Army Air Forces Office of Statistical Control. In 1946 he returned for one semester to Berkeley to receive a doctorate in mathematics, and then he went back to Washington, D.C. , to work for the U.S. Department of Defense .

Equations written on blackboard

While working on allocation of resources (materials and personnel) for various projects and deployments of the U.S. Army Air Forces, Dantzig invented (1947) the simplex algorithm for optimization . At that time such scheduling was called programming, and it soon became apparent that the simplex algorithm was ideal for translating formerly intractable problems involving hundreds, or even thousands, of factors for solution by the recently invented computer . From 1952 to 1960 he was a research mathematician at the RAND Corporation , where he helped develop the field of operations research (essentially, the application of computers to optimization problems). From 1960 to 1966 he served as chairman of the Operations Research Center at Berkeley, and from 1966 until his retirement in 1997 he was a professor of operations research and computer science at Stanford University .

Among Dantzig’s numerous awards were the John von Neumann Theory Prize in operations research (1975), the National Medal of Science (1975), and the National Academy of Sciences Award in applied mathematics and numerical analysis (1977).

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COMMENTS

  1. The Legend of the 'Unsolvable Math Problem'

    George Dantzig recounted his feat in a 1986 interview for the College Mathematics Journal: It happened because during my first year at Berkeley I arrived late one day at one of [Jerzy] Neyman's...

  2. George Dantzig

    In statistics, Dantzig solved two open problems in statistical theory, which he had mistaken for homework after arriving late to a lecture by Jerzy Neyman. [2] At his death, Dantzig was the Professor Emeritus of Transportation Sciences and Professor of Operations Research and of Computer Science at Stanford University . Early life [ edit]

  3. Dantzig's unsolved homework problems

    When Dantzig arrived, he assumed that the two problems were a homework assignment and wrote them down. According to Dantzig, the problems "seemed to be a little harder than usual", but a few days later he handed in completed solutions for the two problems, still believing that they were an assignment that was overdue.

  4. George Bernard Dantzig Solves Unsolvable Math Problems

    Student Solves 'Unsolvable' Statistical Problems Mr. George Bernard Dantzig, a doctoral candidate at the University of California (USC), Berkeley in 1939, arrived late for his graduate-level statistics class and found two problems written upon the blackboard.

  5. A Mathematician Who Solved Major Problems : NPR

    And the professor was just overjoyed because he said, `You know, you've just solved two of the most difficult unsolved problems in statistics. We've got to write them up and submit them for...

  6. Remembering George Dantzig: The real Will Hunting

    Dantzig had no idea what Neyman was talking about until he explained that the two problems on the blackboards were famously unsolved statistical problems — not homework at all. Later on in...

  7. PDF 2 Solving LPs: The Simplex Algorithm of George Dantzig

    2 Solving LPs: The Simplex Algorithm of George Dantzig 2.1 Simplex Pivoting: Dictionary Format ... 3 8 0 x 1,x 2,x 3 In devising our approach we use a standard mathematical approach; reduce the problem to one that we already know how to solve. Since the structure of this problem is essentially linear, we try to reduce it to a problem of solving ...

  8. TIL of George Dantzig, who was late to class and solved two famously

    Six weeks later, Dantzig received a visit from an excited professor Neyman, who was eager to tell him that the homework problems he had solved were two of the most famous unsolved problems in statistics. He had prepared one of Dantzig's solutions for publication in a mathematical journal.

  9. What were the two famous open problems that George Dantzig ...

    What were the two famous open problems that George Dantzig mistook for homework and solved while being a graduate student at Berkeley? All related (31) Sort Recommended Daniel McLaury Former Senior Research Engineer at Peddinghaus (2017-2018) Upvoted by Sridhar Ramesh , ABD on PhD Logic, University of California, Berkeley and Tikhon Jelvis

  10. Student Arrives Late For Class And Solves Famous Unsolved Math Problem

    On the board were two problems of "unsolved" statistics that George mistook for a homework assignment. He copied them down and started working on them from home, six weeks later he turned in the work late, hoping to get at least some credit for the assignment but nothing great.

  11. PDF The Diet Problem

    The Diet Problem GEORGE B. DaNTZIG Department of Operations Research Stanford University Stanford, California 94305-4022 This is a story about connections. ... solve it, he invented a very clever heuristic to arrive at a diet that cost only $39.93 per year (1939 prices). He did not claim it to be the cheapest solution but gave good ...

  12. TIL the mathematician George Dantzig solved two of the most famous

    TIL the mathematician George Dantzig solved two of the most famous problems of statistics, because he came into class too late to hear that they were supposed to be unsolvable ... To make a long story short, the problems on the blackboard which I had solved thinking they were homework were in fact two famous unsolved problems in statistics ...

  13. The truth behind the 'genius'

    According to Dantzig himself, the answer is no: "If I had known that the problems were not homework but were in fact two famous unsolved problems in statistics, I probably would not have...

  14. PhD dissertations that solve an established open problem

    Six weeks later, Dantzig received a visit from an excited professor Neyman, who was eager to tell him that the homework problems he had solved were two of the most famous unsolved problems in statistics. Neyman told Dantzig to wrap the two problems in a binder and he would accept them as a Ph.D. thesis. The two problems that Dantzig solved were ...

  15. Student inadvertently solves never-before-solved math problems

    In 1939, George Dantzig arrived late to his graduate statistics class and saw two problems on the board, not knowing they were examples of problems that had never been solved. He thought they were a homework assignment and was able to solve them. He found out the reality six weeks later when his teacher let him know and helped him publish a ...

  16. Continuous knapsack problem

    The continuous knapsack problem may be solved by a greedy algorithm, first published in 1957 by George Dantzig,[2][3]that considers the materials in sorted order by their values per unit weight. If the sum of the choices made so far equals the capacity W, then the algorithm sets xi = 0.

  17. George Dantzig

    The particular problem solved was one which had been studied earlier by George Stigler ( who later became a Nobel Laureate) who proposed a solution based on the substitution of certain foods by others which gave more nutrition per dollar. He then examined a "handful" of the possible 510 ways to combine the selected foods.

  18. George Dantzig: The Story of The Overlooked Genius

    The two unsolved homework problems that George Dantzig solved The doctoral student George Bernard Dantzig came late to Jerzy Neyman's statistics lecture in 1939, when two homework assignments were already written on the board. He put them in writing and spent many days trying to solve them.

  19. George Dantzig

    George Dantzig, (born Nov. 8, 1914, Portland, Ore., U.S.—died May 13, 2005, Stanford, Calif.), American mathematician who devised the simplex method, an algorithm for solving problems that involve numerous conditions and variables, and in the process founded the field of linear programming.