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## Solving an Assignment Problem

This section presents an example that shows how to solve an assignment problem using both the MIP solver and the CP-SAT solver.

In the example there are five workers (numbered 0-4) and four tasks (numbered 0-3). Note that there is one more worker than in the example in the Overview .

The costs of assigning workers to tasks are shown in the following table.

The problem is to assign each worker to at most one task, with no two workers performing the same task, while minimizing the total cost. Since there are more workers than tasks, one worker will not be assigned a task.

## MIP solution

The following sections describe how to solve the problem using the MPSolver wrapper .

## Import the libraries

The following code imports the required libraries.

## Create the data

The following code creates the data for the problem.

The costs array corresponds to the table of costs for assigning workers to tasks, shown above.

## Declare the MIP solver

The following code declares the MIP solver.

## Create the variables

The following code creates binary integer variables for the problem.

## Create the constraints

Create the objective function.

The following code creates the objective function for the problem.

The value of the objective function is the total cost over all variables that are assigned the value 1 by the solver.

## Invoke the solver

The following code invokes the solver.

## Print the solution

The following code prints the solution to the problem.

Here is the output of the program.

## Complete programs

Here are the complete programs for the MIP solution.

## CP SAT solution

The following sections describe how to solve the problem using the CP-SAT solver.

## Declare the model

The following code declares the CP-SAT model.

The following code sets up the data for the problem.

The following code creates the constraints for the problem.

Here are the complete programs for the CP-SAT solution.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

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## Solving Assignment Problem using Linear Programming in Python

Learn how to use Python PuLP to solve Assignment problems using Linear Programming.

In earlier articles, we have seen various applications of Linear programming such as transportation, transshipment problem, Cargo Loading problem, and shift-scheduling problem. Now In this tutorial, we will focus on another model that comes under the class of linear programming model known as the Assignment problem. Its objective function is similar to transportation problems. Here we minimize the objective function time or cost of manufacturing the products by allocating one job to one machine.

If we want to solve the maximization problem assignment problem then we subtract all the elements of the matrix from the highest element in the matrix or multiply the entire matrix by –1 and continue with the procedure. For solving the assignment problem, we use the Assignment technique or Hungarian method, or Flood’s technique.

The transportation problem is a special case of the linear programming model and the assignment problem is a special case of transportation problem, therefore it is also a special case of the linear programming problem.

In this tutorial, we are going to cover the following topics:

## Assignment Problem

A problem that requires pairing two sets of items given a set of paired costs or profit in such a way that the total cost of the pairings is minimized or maximized. The assignment problem is a special case of linear programming.

For example, an operation manager needs to assign four jobs to four machines. The project manager needs to assign four projects to four staff members. Similarly, the marketing manager needs to assign the 4 salespersons to 4 territories. The manager’s goal is to minimize the total time or cost.

## Problem Formulation

A manager has prepared a table that shows the cost of performing each of four jobs by each of four employees. The manager has stated his goal is to develop a set of job assignments that will minimize the total cost of getting all 4 jobs.

## Initialize LP Model

In this step, we will import all the classes and functions of pulp module and create a Minimization LP problem using LpProblem class.

## Define Decision Variable

In this step, we will define the decision variables. In our problem, we have two variable lists: workers and jobs. Let’s create them using LpVariable.dicts() class. LpVariable.dicts() used with Python’s list comprehension. LpVariable.dicts() will take the following four values:

- First, prefix name of what this variable represents.
- Second is the list of all the variables.
- Third is the lower bound on this variable.
- Fourth variable is the upper bound.
- Fourth is essentially the type of data (discrete or continuous). The options for the fourth parameter are LpContinuous or LpInteger .

Let’s first create a list route for the route between warehouse and project site and create the decision variables using LpVariable.dicts() the method.

## Define Objective Function

In this step, we will define the minimum objective function by adding it to the LpProblem object. lpSum(vector)is used here to define multiple linear expressions. It also used list comprehension to add multiple variables.

## Define the Constraints

Here, we are adding two types of constraints: Each job can be assigned to only one employee constraint and Each employee can be assigned to only one job. We have added the 2 constraints defined in the problem by adding them to the LpProblem object.

## Solve Model

In this step, we will solve the LP problem by calling solve() method. We can print the final value by using the following for loop.

From the above results, we can infer that Worker-1 will be assigned to Job-1, Worker-2 will be assigned to job-3, Worker-3 will be assigned to Job-2, and Worker-4 will assign with job-4.

In this article, we have learned about Assignment problems, Problem Formulation, and implementation using the python PuLp library. We have solved the Assignment problem using a Linear programming problem in Python. Of course, this is just a simple case study, we can add more constraints to it and make it more complicated. You can also run other case studies on Cargo Loading problems , Staff scheduling problems . In upcoming articles, we will write more on different optimization problems such as transshipment problem, balanced diet problem. You can revise the basics of mathematical concepts in this article and learn about Linear Programming in this article .

- Solving Blending Problem in Python using Gurobi
- Transshipment Problem in Python Using PuLP

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Problem Set and Programming Assignment Solutions to Stanford University's Algorithms Specialization on Coursera & edX

## liuhh02/stanford-algorithms-specialization

Folders and files, repository files navigation, stanford-algorithms-specialization.

Problem Set and Programming Assignment Solutions in C++ to Stanford University's Algorithms Specialization on Coursera & edX .

Instructor : Tim Roughgarden

## Introduction

This repository contains the problem set and programming assignment solutions in C++ to the specialization. On Coursera, the specialization consists of four courses.

Date Started: 14 February 2021

Date Completed: 14 April 2021

The problem set and programming assignment solutions are uploaded only for reference purposes. Please attempt the quizzes and programming assignments yourself and only look at the explanations if you find that you still can't understand it after consulting the discussion forums and reviewing the lecture content.

## Quick Access

Course 1: divide and conquer, sorting and searching, and randomized algorithms, progress: 14 february 2021 - 17 february 2021 (completed).

- Programming Assignment
- Problem Set
- Problem Set (hide answers)
- Final Exam (hide answers)

## Course 2: Graph Search, Shortest Paths, and Data Structures

Progress: 25 february 2021 - 2 march 2021 (completed), course 3: greedy algorithms, minimum spanning trees, and dynamic programming, progress: 9 march 2021 - 14 march 2021 (completed), course 4: shortest paths revisited, np-complete problems and what to do about them, progress: 5 april 2021 - 14 april 2021 (completed), contributors 2.

## How to Solve the Assignment Problem: A Complete Guide

Table of Contents

Assignment problem is a special type of linear programming problem that deals with assigning a number of resources to an equal number of tasks in the most efficient way. The goal is to minimize the total cost of assignments while ensuring that each task is assigned to only one resource and each resource is assigned to only one task. In this blog, we will discuss the solution of the assignment problem using the Hungarian method, which is a popular algorithm for solving the problem.

## Understanding the Assignment Problem

Before we dive into the solution, it is important to understand the problem itself. In the assignment problem, we have a matrix of costs, where each row represents a resource and each column represents a task. The objective is to assign each resource to a task in such a way that the total cost of assignments is minimized. However, there are certain constraints that need to be satisfied – each resource can be assigned to only one task and each task can be assigned to only one resource.

## Solving the Assignment Problem

There are various methods for solving the assignment problem, including the Hungarian method, the brute force method, and the auction algorithm. Here, we will focus on the steps involved in solving the assignment problem using the Hungarian method, which is the most commonly used and efficient method.

## Step 1: Set up the cost matrix

The first step in solving the assignment problem is to set up the cost matrix, which represents the cost of assigning a task to an agent. The matrix should be square and have the same number of rows and columns as the number of tasks and agents, respectively.

## Step 2: Subtract the smallest element from each row and column

To simplify the calculations, we need to reduce the size of the cost matrix by subtracting the smallest element from each row and column. This step is called matrix reduction.

## Step 3: Cover all zeros with the minimum number of lines

The next step is to cover all zeros in the matrix with the minimum number of horizontal and vertical lines. This step is called matrix covering.

## Step 4: Test for optimality and adjust the matrix

To test for optimality, we need to calculate the minimum number of lines required to cover all zeros in the matrix. If the number of lines equals the number of rows or columns, the solution is optimal. If not, we need to adjust the matrix and repeat steps 3 and 4 until we get an optimal solution.

## Step 5: Assign the tasks to the agents

The final step is to assign the tasks to the agents based on the optimal solution obtained in step 4. This will give us the most cost-effective or profit-maximizing assignment.

## Solution of the Assignment Problem using the Hungarian Method

The Hungarian method is an algorithm that uses a step-by-step approach to find the optimal assignment. The algorithm consists of the following steps:

- Subtract the smallest entry in each row from all the entries of the row.
- Subtract the smallest entry in each column from all the entries of the column.
- Draw the minimum number of lines to cover all zeros in the matrix. If the number of lines drawn is equal to the number of rows, we have an optimal solution. If not, go to step 4.
- Determine the smallest entry not covered by any line. Subtract it from all uncovered entries and add it to all entries covered by two lines. Go to step 3.

The above steps are repeated until an optimal solution is obtained. The optimal solution will have all zeros covered by the minimum number of lines. The assignments can be made by selecting the rows and columns with a single zero in the final matrix.

## Applications of the Assignment Problem

The assignment problem has various applications in different fields, including computer science, economics, logistics, and management. In this section, we will provide some examples of how the assignment problem is used in real-life situations.

## Applications in Computer Science

The assignment problem can be used in computer science to allocate resources to different tasks, such as allocating memory to processes or assigning threads to processors.

## Applications in Economics

The assignment problem can be used in economics to allocate resources to different agents, such as allocating workers to jobs or assigning projects to contractors.

## Applications in Logistics

The assignment problem can be used in logistics to allocate resources to different activities, such as allocating vehicles to routes or assigning warehouses to customers.

## Applications in Management

The assignment problem can be used in management to allocate resources to different projects, such as allocating employees to tasks or assigning budgets to departments.

Let’s consider the following scenario: a manager needs to assign three employees to three different tasks. Each employee has different skills, and each task requires specific skills. The manager wants to minimize the total time it takes to complete all the tasks. The skills and the time required for each task are given in the table below:

The assignment problem is to determine which employee should be assigned to which task to minimize the total time required. To solve this problem, we can use the Hungarian method, which we discussed in the previous blog.

Using the Hungarian method, we first subtract the smallest entry in each row from all the entries of the row:

Next, we subtract the smallest entry in each column from all the entries of the column:

We draw the minimum number of lines to cover all the zeros in the matrix, which in this case is three:

Since the number of lines is equal to the number of rows, we have an optimal solution. The assignments can be made by selecting the rows and columns with a single zero in the final matrix. In this case, the optimal assignments are:

- Emp 1 to Task 3
- Emp 2 to Task 2
- Emp 3 to Task 1

This assignment results in a total time of 9 units.

I hope this example helps you better understand the assignment problem and how to solve it using the Hungarian method.

Solving the assignment problem may seem daunting, but with the right approach, it can be a straightforward process. By following the steps outlined in this guide, you can confidently tackle any assignment problem that comes your way.

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## Operations Research

1 Operations Research-An Overview

- History of O.R.
- Approach, Techniques and Tools
- Phases and Processes of O.R. Study
- Typical Applications of O.R
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- Models in Operations Research
- O.R. in real world

2 Linear Programming: Formulation and Graphical Method

- General formulation of Linear Programming Problem
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- Solving Linear Programming Graphically Using Computer
- Application of Linear Programming in Business and Industry

3 Linear Programming-Simplex Method

- Principle of Simplex Method
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- Simplex Method with several Decision Variables
- Two Phase and M-method
- Multiple Solution, Unbounded Solution and Infeasible Problem
- Sensitivity Analysis
- Dual Linear Programming Problem

4 Transportation Problem

- Basic Feasible Solution of a Transportation Problem
- Modified Distribution Method
- Stepping Stone Method
- Unbalanced Transportation Problem
- Degenerate Transportation Problem
- Transhipment Problem
- Maximisation in a Transportation Problem

5 Assignment Problem

- Solution of the Assignment Problem
- Unbalanced Assignment Problem
- Problem with some Infeasible Assignments
- Maximisation in an Assignment Problem
- Crew Assignment Problem

6 Application of Excel Solver to Solve LPP

- Building Excel model for solving LP: An Illustrative Example

7 Goal Programming

- Concepts of goal programming
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- The simplex method of goal programming
- Using Excel Solver to Solve Goal Programming Models
- Application areas of goal programming

8 Integer Programming

- Some Integer Programming Formulation Techniques
- Binary Representation of General Integer Variables
- Unimodularity
- Cutting Plane Method
- Branch and Bound Method
- Solver Solution

9 Dynamic Programming

- Dynamic Programming Methodology: An Example
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10 Non-Linear Programming

- Solution of a Non-linear Programming Problem
- Convex and Concave Functions
- Kuhn-Tucker Conditions for Constrained Optimisation
- Quadratic Programming
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11 Introduction to game theory and its Applications

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12 Monte Carlo Simulation

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13 Queueing Models

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## Quantitative Techniques: Theory and Problems by P. C. Tulsian, Vishal Pandey

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## WHAT IS ASSIGNMENT PROBLEM

Assignment Problem is a special type of linear programming problem where the objective is to minimise the cost or time of completing a number of jobs by a number of persons.

The assignment problem in the general form can be stated as follows:

“Given n facilities, n jobs and the effectiveness of each facility for each job, the problem is to assign each facility to one and only one job in such a way that the measure of effectiveness is optimised (Maximised or Minimised).”

Several problems of management has a structure identical with the assignment problem.

Example I A manager has four persons (i.e. facilities) available for four separate jobs (i.e. jobs) and the cost of assigning (i.e. effectiveness) each job to each ...

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Assignment problem in linear programming : introduction and assignment model.

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Assignment problem is a special type of linear programming problem which deals with the allocation of the various resources to the various activities on one to one basis. It does it in such a way that the cost or time involved in the process is minimum and profit or sale is maximum. Though there problems can be solved by simplex method or by transportation method but assignment model gives a simpler approach for these problems.

In a factory, a supervisor may have six workers available and six jobs to fire. He will have to take decision regarding which job should be given to which worker. Problem forms one to one basis. This is an assignment problem.

## 1. Assignment Model :

Suppose there are n facilitates and n jobs it is clear that in this case, there will be n assignments. Each facility or say worker can perform each job, one at a time. But there should be certain procedure by which assignment should be made so that the profit is maximized or the cost or time is minimized.

In the table, Co ij is defined as the cost when j th job is assigned to i th worker. It maybe noted here that this is a special case of transportation problem when the number of rows is equal to number of columns.

## Mathematical Formulation:

Any basic feasible solution of an Assignment problem consists (2n – 1) variables of which the (n – 1) variables are zero, n is number of jobs or number of facilities. Due to this high degeneracy, if we solve the problem by usual transportation method, it will be a complex and time consuming work. Thus a separate technique is derived for it. Before going to the absolute method it is very important to formulate the problem.

Suppose x jj is a variable which is defined as

1 if the i th job is assigned to j th machine or facility

0 if the i th job is not assigned to j th machine or facility.

Now as the problem forms one to one basis or one job is to be assigned to one facility or machine.

The total assignment cost will be given by

The above definition can be developed into mathematical model as follows:

Determine x ij > 0 (i, j = 1,2, 3…n) in order to

Subjected to constraints

and x ij is either zero or one.

## Method to solve Problem (Hungarian Technique):

Consider the objective function of minimization type. Following steps are involved in solving this Assignment problem,

1. Locate the smallest cost element in each row of the given cost table starting with the first row. Now, this smallest element is subtracted form each element of that row. So, we will be getting at least one zero in each row of this new table.

2. Having constructed the table (as by step-1) take the columns of the table. Starting from first column locate the smallest cost element in each column. Now subtract this smallest element from each element of that column. Having performed the step 1 and step 2, we will be getting at least one zero in each column in the reduced cost table.

3. Now, the assignments are made for the reduced table in following manner.

(i) Rows are examined successively, until the row with exactly single (one) zero is found. Assignment is made to this single zero by putting square □ around it and in the corresponding column, all other zeros are crossed out (x) because these will not be used to make any other assignment in this column. Step is conducted for each row.

(ii) Step 3 (i) in now performed on the columns as follow:- columns are examined successively till a column with exactly one zero is found. Now , assignment is made to this single zero by putting the square around it and at the same time, all other zeros in the corresponding rows are crossed out (x) step is conducted for each column.

(iii) Step 3, (i) and 3 (ii) are repeated till all the zeros are either marked or crossed out. Now, if the number of marked zeros or the assignments made are equal to number of rows or columns, optimum solution has been achieved. There will be exactly single assignment in each or columns without any assignment. In this case, we will go to step 4.

4. At this stage, draw the minimum number of lines (horizontal and vertical) necessary to cover all zeros in the matrix obtained in step 3, Following procedure is adopted:

(iii) Now tick mark all the rows that are not already marked and that have assignment in the marked columns.

(iv) All the steps i.e. (4(i), 4(ii), 4(iii) are repeated until no more rows or columns can be marked.

(v) Now draw straight lines which pass through all the un marked rows and marked columns. It can also be noticed that in an n x n matrix, always less than ‘n’ lines will cover all the zeros if there is no solution among them.

5. In step 4, if the number of lines drawn are equal to n or the number of rows, then it is the optimum solution if not, then go to step 6.

6. Select the smallest element among all the uncovered elements. Now, this element is subtracted from all the uncovered elements and added to the element which lies at the intersection of two lines. This is the matrix for fresh assignments.

7. Repeat the procedure from step (3) until the number of assignments becomes equal to the number of rows or number of columns.

## Related Articles:

- Two Phase Methods of Problem Solving in Linear Programming: First and Second Phase
- Linear Programming: Applications, Definitions and Problems

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## Assignment Model | Linear Programming Problem (LPP) | Introduction

What is assignment model.

→ Assignment model is a special application of Linear Programming Problem (LPP) , in which the main objective is to assign the work or task to a group of individuals such that;

i) There is only one assignment.

ii) All the assignments should be done in such a way that the overall cost is minimized (or profit is maximized, incase of maximization).

→ In assignment problem, the cost of performing each task by each individual is known. → It is desired to find out the best assignments, such that overall cost of assigning the work is minimized.

## For example:

Suppose there are 'n' tasks, which are required to be performed using 'n' resources.

The cost of performing each task by each resource is also known (shown in cells of matrix)

- In the above asignment problem, we have to provide assignments such that there is one to one assignments and the overall cost is minimized.

## How Assignment Problem is related to LPP? OR Write mathematical formulation of Assignment Model.

→ Assignment Model is a special application of Linear Programming (LP).

→ The mathematical formulation for Assignment Model is given below:

→ Let, C i j \text {C}_{ij} C ij denotes the cost of resources 'i' to the task 'j' ; such that

→ Now assignment problems are of the Minimization type. So, our objective function is to minimize the overall cost.

→ Subjected to constraint;

(i) For all j t h j^{th} j t h task, only one i t h i^{th} i t h resource is possible:

(ii) For all i t h i^{th} i t h resource, there is only one j t h j^{th} j t h task possible;

(iii) x i j x_{ij} x ij is '0' or '1'.

## Types of Assignment Problem:

(i) balanced assignment problem.

- It consist of a suqare matrix (n x n).
- Number of rows = Number of columns

## (ii) Unbalanced Assignment Problem

- It consist of a Non-square matrix.
- Number of rows ≠ \not= = Number of columns

## Methods to solve Assignment Model:

(i) integer programming method:.

In assignment problem, either allocation is done to the cell or not.

So this can be formulated using 0 or 1 integer.

While using this method, we will have n x n decision varables, and n+n equalities.

So even for 4 x 4 matrix problem, it will have 16 decision variables and 8 equalities.

So this method becomes very lengthy and difficult to solve.

## (ii) Transportation Methods:

As assignment problem is a special case of transportation problem, it can also be solved using transportation methods.

In transportation methods ( NWCM , LCM & VAM), the total number of allocations will be (m+n-1) and the solution is known as non-degenerated. (For eg: for 3 x 3 matrix, there will be 3+3-1 = 5 allocations)

But, here in assignment problems, the matrix is a square matrix (m=n).

So total allocations should be (n+n-1), i.e. for 3 x 3 matrix, it should be (3+3-1) = 5

But, we know that in 3 x 3 assignment problem, maximum possible possible assignments are 3 only.

So, if are we will use transportation methods, then the solution will be degenerated as it does not satisfy the condition of (m+n-1) allocations.

So, the method becomes lengthy and time consuming.

## (iii) Enumeration Method:

It is a simple trail and error type method.

Consider a 3 x 3 assignment problem. Here the assignments are done randomly and the total cost is found out.

For 3 x 3 matrix, the total possible trails are 3! So total 3! = 3 x 2 x 1 = 6 trails are possible.

The assignments which gives minimum cost is selected as optimal solution.

But, such trail and error becomes very difficult and lengthy.

If there are more number of rows and columns, ( For eg: For 6 x 6 matrix, there will be 6! trails. So 6! = 6 x 5 x 4 x 3 x 2 x 1 = 720 trails possible) then such methods can't be applied for solving assignments problems.

## (iv) Hungarian Method:

It was developed by two mathematicians of Hungary. So, it is known as Hungarian Method.

It is also know as Reduced matrix method or Flood's technique.

There are two main conditions for applying Hungarian Method:

(1) Square Matrix (n x n). (2) Problem should be of minimization type.

## Suggested Notes:

## Modified Distribution Method (MODI) | Transportation Problem | Transportation Model

## Stepping Stone | Transportation Problem | Transportation Model

## Vogel’s Approximation Method (VAM) | Method to Solve Transportation Problem | Transportation Model

## Transportation Model - Introduction

## North West Corner Method | Method to Solve Transportation Problem | Transportation Model

## Least Cost Method | Method to Solve Transportation Problem | Transportation Model

## Tie in selecting row and column (Vogel's Approximation Method - VAM) | Numerical | Solving Transportation Problem | Transportation Model

## Crashing Special Case - Multiple (Parallel) Critical Paths

## Crashing Special Case - Indirect cost less than Crash Cost

## Basics of Program Evaluation and Review Technique (PERT)

## Numerical on PERT (Program Evaluation and Review Technique)

## Network Analysis - Dealing with Network Construction Basics

## Construct a project network with predecessor relationship | Operation Research | Numerical

## Graphical Method | Methods to solve LPP | Linear Programming

## Basics of Linear Programming

## Linear Programming Problem (LPP) Formulation with Numericals

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- N Queen Problem using Branch And Bound
- Traveling Salesman Problem using Branch And Bound

Let there be N workers and N jobs. Any worker can be assigned to perform any job, incurring some cost that may vary depending on the work-job assignment. It is required to perform all jobs by assigning exactly one worker to each job and exactly one job to each agent in such a way that the total cost of the assignment is minimized.

Let us explore all approaches for this problem.

## Solution 1: Brute Force

We generate n! possible job assignments and for each such assignment, we compute its total cost and return the less expensive assignment. Since the solution is a permutation of the n jobs, its complexity is O(n!).

## Solution 2: Hungarian Algorithm

The optimal assignment can be found using the Hungarian algorithm. The Hungarian algorithm has worst case run-time complexity of O(n^3).

## Solution 3: DFS/BFS on state space tree

A state space tree is a N-ary tree with property that any path from root to leaf node holds one of many solutions to given problem. We can perform depth-first search on state space tree and but successive moves can take us away from the goal rather than bringing closer. The search of state space tree follows leftmost path from the root regardless of initial state. An answer node may never be found in this approach. We can also perform a Breadth-first search on state space tree. But no matter what the initial state is, the algorithm attempts the same sequence of moves like DFS.

## Solution 4: Finding Optimal Solution using Branch and Bound

The selection rule for the next node in BFS and DFS is “blind”. i.e. the selection rule does not give any preference to a node that has a very good chance of getting the search to an answer node quickly. The search for an optimal solution can often be speeded by using an “intelligent” ranking function, also called an approximate cost function to avoid searching in sub-trees that do not contain an optimal solution. It is similar to BFS-like search but with one major optimization. Instead of following FIFO order, we choose a live node with least cost. We may not get optimal solution by following node with least promising cost, but it will provide very good chance of getting the search to an answer node quickly.

There are two approaches to calculate the cost function:

- For each worker, we choose job with minimum cost from list of unassigned jobs (take minimum entry from each row).
- For each job, we choose a worker with lowest cost for that job from list of unassigned workers (take minimum entry from each column).

In this article, the first approach is followed.

Let’s take below example and try to calculate promising cost when Job 2 is assigned to worker A.

Since Job 2 is assigned to worker A (marked in green), cost becomes 2 and Job 2 and worker A becomes unavailable (marked in red).

Now we assign job 3 to worker B as it has minimum cost from list of unassigned jobs. Cost becomes 2 + 3 = 5 and Job 3 and worker B also becomes unavailable.

Finally, job 1 gets assigned to worker C as it has minimum cost among unassigned jobs and job 4 gets assigned to worker D as it is only Job left. Total cost becomes 2 + 3 + 5 + 4 = 14.

Below diagram shows complete search space diagram showing optimal solution path in green.

Complete Algorithm:

Below is the implementation of the above approach:

Time Complexity: O(M*N). This is because the algorithm uses a double for loop to iterate through the M x N matrix. Auxiliary Space: O(M+N). This is because it uses two arrays of size M and N to track the applicants and jobs.

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## How MSNBC’s Leftward Tilt Delivers Ratings, and Complications

NBC’s leaders have been forced to grapple with how to square its cable news network’s embrace of progressive politics with the company’s straight-news operation.

By Jim Rutenberg and Michael M. Grynbaum

MSNBC placed a big bet on becoming comfort TV for liberals. Then it doubled down.

Time slots on the cable network once devoted to news programming are now occupied by Trump-bashing opinion hosts. The channel has become a landing spot for high-profile alumni of President Biden’s administration like Jen Psaki, who went from hosting White House press briefings to hosting her own show. On Super Tuesday, when producers aired a portion of a live speech by former President Donald J. Trump, Rachel Maddow chastised her bosses on the air.

The moves have been a hit with viewers. MSNBC has leapfrogged past its erstwhile rival CNN in the ratings and has seen viewership rise over the past year, securing second place in cable news behind the perennial leader, Fox News.

But MSNBC’s success has had unintended consequences for its parent company, NBC, an original Big Three broadcaster that still strives to appeal to a mass American audience.

NBC’s traditional political journalists have cycled between rancor and resignation that the cable network’s partisanship — a regular target of Mr. Trump — will color perceptions of their straight news reporting. Local NBC stations between the coasts have demanded, again and again, that executives in New York do more to preserve NBC’s nonpartisan brand, lest MSNBC’s blue-state bent alienate their red-state viewers.

Even Comcast, NBC’s corporate owner, which is loath to intervene in news coverage, took the rare step of conveying its concern to MSNBC’s leaders when some hosts and guests criticized Israel as the Hamas attack was unfolding on Oct. 7, according to three people with knowledge of the discussions. An abrupt course correction to that coverage followed.

This account of the tensions roiling NBC and its corporate overseers is based on interviews with more than two dozen people with knowledge of the company’s inner workings, almost all of whom insisted on anonymity to share details of internal discussions.

NBC declined to make its top executives available for interviews. The chairman of the NBCUniversal News Group, Cesar Conde, has said he wants his division — which encompasses MSNBC, CNBC, a digital streaming service, Telemundo and journalistic stalwarts like “Nightly News,” “Meet the Press” and “Today” — to be a big tent.

Yet his recent efforts to include more conservative voices on the airwaves generated newsroom suspicion and ultimately led to an embarrassing rebellion over the hiring of Ronna McDaniel, a former Republican Party chair who aided Mr. Trump’s attempt to overturn his 2020 election loss.

MSNBC hosts, for their part, view their role in the political debate as more important than ever. They dismiss the accusation that MSNBC is a “Fox News for Democrats” and say their message — that Mr. Trump’s candidacy represents a unique and clear threat to democracy — is an urgent one for the electorate to hear.

And executives inside NBC’s corporate suites at Rockefeller Center say they are confident that viewers know the differences between the company’s various news brands. Any related challenges, they argue, are of a high-class sort — because their cable channels give NBC an advantage in relevance and revenue over its original Big Three competitors, ABC and CBS, which have no cable presence.

“Our strategy is built on our distinct, complementary brands including NBC News, CNBC, NBC News Now, MSNBC and Telemundo,” the NBCUniversal News Group said in a statement. “That has driven our performance as the nation’s leading news organization with the largest reach.” (Comcast does not disclose the news division’s earnings in its reports to Wall Street.)

The tensions inside NBC are, in some ways, a microcosm of the challenges facing many traditional news organizations as the country hurtles toward a tense presidential election: how to maintain trust and present neutral, fact-based reporting in a fractionalized era when partisanship carries vast financial and cultural rewards.

But the company’s challenge is also unique. It must juggle a broadcast news operation bound by traditional standards of impartiality and a cable channel increasingly bound by the partisan preferences of an intensely loyal viewership. How NBC navigates these dueling imperatives will have important implications for Comcast, a Philadelphia-based conglomerate known for its aversion to the political spotlight.

It will also have consequences for coverage of the presidential campaign. Where MSNBC’s cable news opinion-makers sustain and galvanize the Democratic faithful, the NBC broadcast network reaches millions of the potentially persuadable voters critical to both parties, which have sought to turn NBC’s internal tensions to their own advantage.

## Left, Right, Left

MSNBC has caused corporate headaches since its inception.

NBC formed the channel as a joint venture with Microsoft in 1996 with the hope that it would thrust “all the value of NBC News into the cable world,” as Tom Rogers, a former NBC executive who helped found the cable network, described it in an interview.

But critics mocked the new 24-hour channel for its informal approach to news, mixing NBC’s biggest stars with younger personalities on a set reminiscent of Central Perk on “Friends.” It was almost immediately outflanked by Fox News, which followed MSNBC to market that same year and rose to the top of the cable news ratings as the first 24-hour TV channel with an overt political appeal.

MSNBC struggled with its identity. It moved to the left ahead of the Iraq war — and later moved right by hiring new hosts like the former Republican congressman Joe Scarborough. Soon it shifted leftward again, as the host Keith Olbermann hit a nerve with his strident anti-Bush — and often anti-Fox — commentary.

But when Andrew Lack, a veteran producer, took over NBC’s news division in 2015, he decided the channel needed to tone down its partisan image. Under Mr. Lack — who oversaw MSNBC’s creation in an earlier NBC stint — the cable network bumped the Rev. Al Sharpton from the weekday schedule, hired the former Fox anchor Greta Van Susteren and added more straightforward news programs, including a daily version of “Meet the Press,” NBC’s flagship political show, with Chuck Todd.

Mr. Todd was game — but would come to believe that his MSNBC duties ultimately hurt the “Meet the Press” franchise, several people at NBC said in interviews. The daily version of the show fell increasingly out of step with MSNBC’s partisan slant even as Republicans used its association with the liberal cable network to deny interview requests from the flagship Sunday edition of “Meet the Press.”

Then, Mr. Trump’s ascent shocked the Democratic base and spiked viewership of Ms. Maddow and other left-leaning hosts, whose programs became a kind of televised safe space. MSNBC’s ratings surged .

## Conde Faces the Messiness

Mr. Conde succeeded Mr. Lack in spring 2020. A Wharton-trained business executive who sits on the boards of Walmart and PepsiCo, he came up through the corporate side of news, having led a turnaround at Telemundo after serving as the president of Univision Networks. Accordingly, Mr. Conde was expected to impose a more disciplined and neater corporate sensibility to the division.

He was almost immediately confronted by the messiness he had inherited.

Within a few weeks of Mr. Conde’s ascension, Mr. Trump attacked NBC when it announced the hiring of a new contributor: Lisa Page, a former F.B.I. lawyer who became a lightning rod on the right for her role in the investigation into his campaign ties to Russia. After an initial MSNBC appearance she did not show up again.

A few months later, NBC faced criticism from the other direction when it booked Mr. Trump for a prime-time interview on the night of a presidential debate that he had boycotted. (Mr. Biden was appearing at the same time on ABC.) Ms. Maddow chastised her bosses about it on the air.

That sort of partisan tumult has often riled another important constituency for Mr. Conde: NBC’s affiliated regional stations, which the company relies on to carry its major news programs to markets throughout the country.

The stations tend to be deeply embedded — and deeply trusted — in their communities. Many of them operate in red states or counties and chafed whenever MSNBC, which Mr. Trump regularly calls “MSDNC,” drew conservative ire.

Over the years the affiliates, many of which would have been thrilled to see MSNBC’s leftward tilt abandoned entirely, increasingly urged NBC executives to better distinguish its content from the NBC journalism like “Today” and “Nightly News” that they carried on their stations.

At one point after Mr. Conde took over, executives talked about the possibility of doubling down on partisanship and stripping MSNBC of news altogether, defining it as a pure opinion channel. The company would use the new NBC News Now streaming service, started under Noah Oppenheim when he was NBC News president, for 24-hour news, according to two people with knowledge of the conversations.

That idea fizzled. Mr. Conde was not prepared to entirely abandon news, but he began to better distinguish the various parts of his news division — which effectively moved MSNBC and NBC News further apart.

In the Lack era, Mr. Oppenheim of NBC News and Phil Griffin, the longtime chief of MSNBC, often worked closely as they managed a collection of stars who worked for both networks, like Mr. Todd, Craig Melvin and Hallie Jackson.

Creating more distance between the cable and broadcast outlets, Mr. Conde and Mr. Griffin’s successor, Rashida Jones, moved Mr. Todd, Ms. Jackson and Mr. Melvin off MSNBC to work exclusively at NBC News and NBC News Now. MSNBC’s daytime block of hard news shrank to six hours from eight, as the cable network extended by an hour each two opinion shows with loyal followings: “Morning Joe” featuring Mr. Scarborough and his wife Mika Brzezinski, and “Deadline: White House” with Nicolle Wallace as host.

Nothing did more to signal that MSNBC was more tightly embracing its partisan direction than Ms. Jones’s decision to hire Ms. Psaki and another Biden aide, Symone D. Sanders, straight from the White House.

It was the kind of revolving-door hiring that liberal pundits used to criticize when it happened with Fox News and the Trump administration.

It also created an awkward situation for the NBC News White House team, which was caught off guard when word that Ms. Psaki was in talks for the job leaked while she was still serving as White House press secretary.

A tense, televised confrontation followed in the White House briefing room when Kristen Welker, then NBC News’s co-chief White House correspondent, asked her future colleague: “How is it ethical to have these conversations with media outlets while you continue to have a job standing behind that podium?”

## Chasing a Broad Appeal

At the same time, NBC News was going through its own changes.

Early last year, Mr. Oppenheim left his post running NBC News, and Mr. Conde split his job in three. In a jigsaw-like structure, one executive now oversaw “Today,” another “Nightly News” and NBC News Now, and a third “Meet the Press,” “Dateline” and news coverage across numerous shows and platforms.

Mr. Conde said the new setup would provide “growth opportunities,” with each show acting like its own megafranchise. “Today,” for instance, includes an e-commerce business and online sites dedicated to cooking, wellness and books.

He gave his deputies another brief: making additional efforts to ensure that news coverage reflected a wider range of political viewpoints.

Mr. Conde wanted to get Republicans back onto shows.

That was in line with an industrywide recalibration. After four years of combat between the press and Mr. Trump, media companies have sought better ways to reach Trump supporters who feel alienated from mainstream news. Television executives were also concerned that Republican elected officials were shunning their shows in favor of the congenial confines of right-wing media.

It was especially thorny for NBC, as Mr. Trump continued to yoke NBC News to MSNBC while accusing them, along with Comcast, of committing “Country Threatening Treason.”

A chance for a fresh start seemed to come last September when Ms. Welker succeeded Mr. Todd as the moderator of “Meet the Press.”

According to several people with knowledge of the internal discussions, Mr. Conde and Ms. Welker agreed that she should make booking both Mr. Trump and Mr. Biden for interviews a priority. Mr. Biden declined; Mr. Trump accepted.

But when Mr. Conde said she should schedule the Trump interview for her debut episode, Ms. Welker disagreed. Questioning the mendacious former president can be a high-wire act for even the most experienced TV interviewers, and Ms. Welker did not think it was a wise way to introduce herself to viewers. She acquiesced only after coaxing from Mr. Conde and several of his deputies.

Ms. Welker worked to fact-check Mr. Trump in real time while also eliciting an admission that he ignored his own campaign lawyers when they told him there was no evidence the 2020 presidential election results were rigged. Mr. Trump steamrolled ahead with a litany of lies nonetheless. The interview was panned on social media — complete with a “#boycottmeetthepress” campaign — but was deemed a success by Mr. Conde.

Mr. Conde and Rebecca Blumenstein, a former editor at The New York Times whom Mr. Conde hired as one of his top deputies, also worked aggressively to secure a Republican primary debate in fall 2023, pitching Ms. McDaniel and other Republican officials in person.

They succeeded, but only after accepting terms that unsettled some journalists within the company. NBC agreed to include a moderator from a right-wing media company, Salem Radio, and stream the debate live on Rumble, a video site that frequently hosts pro-Nazi and other extremist content. (NBC executives have defended the decision, noting that Rumble was already the party’s official streamer and had no editorial input.)

The debate received good marks in the press. And in general, red-state affiliates felt that Mr. Conde was doing a better job of bringing balance to NBC News, according to an executive at one company that owns affiliates.

## Reverberations Continue

Each network was now set on its own distinct course: MSNBC toward more partisan and progressive opinion, and NBC News toward Mr. Conde’s commitment to “presenting our audiences with a widely diverse set of viewpoints and experiences,” as he put it.

But each tripped over the limits of its approach in an election landscape already littered with ideological tripwires.

When Hamas staged its terror attack against Israel on Oct. 7, MSNBC mixed breaking news of the attacks with discussions about the historical backdrop of Israel’s treatment of Palestinians. The coverage reflected views on the left — and presaged the pro-Palestinian demonstrations that would soon grow in number — but it struck many others as discordant, or even offensive, given that the violence was still coming into view.

“I love this network, but I’ve got to ask: Who’s writing your scripts? Hamas?” Jonathan Greenblatt, the Anti-Defamation League chief executive, asked two days later on “Morning Joe.”

Some of the blowback came from within.

In a call with Mr. Conde, Michael Cavanagh, the president of Comcast, who oversees NBC, shared concerns about that initial coverage, according to three people with knowledge of the discussions. Mr. Conde harbored the same concerns, according to a person briefed on their conversation, and he directed MSNBC to be more circumspect and to focus on facts, not opinions, in those initial days.

Five months later, Mr. Conde thought he had achieved a milestone at NBC News in his efforts to integrate right-wing perspectives into its programming. At the recommendation of Ms. Blumenstein and Carrie Budoff Brown, who oversees political coverage, Mr. Conde hired Ms. McDaniel, the former Republican Party chair, as a contributor who could offer on-air commentary.

If the hiring was in service of Mr. Conde’s goal of adding balance, it came as an unwelcome surprise to NBC’s ranks of correspondents, hosts and anchors. Ms. Welker had booked Ms. McDaniel for her next episode of “Meet the Press” — as a guest, not as a colleague. In the interview, she grilled Ms. McDaniel about her role in Mr. Trump’s effort to overturn the 2020 election result, actions that many at NBC and MSNBC viewed as disqualifying for a job there.

Mr. Todd, appearing as a guest on that day’s episode, unleashed a live, on-air denunciation of his bosses after the interview that left the control room in stunned silence. His rebellion carried over the next day on MSNBC, from “Morning Joe” up through “The Rachel Maddow Show.” Under pressure, Mr. Conde broke the deal with Ms. McDaniel, a move that only served to upset the Republicans he was trying to attract.

In the aftermath, NBC’s public stumble turned into a point of contention on the presidential campaign trail. The Republican Party said it was weighing an attempt to restrict NBC News at this summer’s convention, while Mr. Trump yet again bashed “Fake News NBC.”

Aides to Mr. Biden were also perturbed about the McDaniel hire, viewing it as part of a broader attempt by NBC News to overcompensate for MSNBC’s decidedly pro-Biden stance. In private conversations with NBC correspondents, Biden aides have argued that “Nightly News,” whose huge audience is of critical political importance to the campaign, was taking it easy on Mr. Trump and treating Mr. Biden too harshly.

Executives at NBC dismissed these complaints, saying the partisan brickbats simply come with the territory. They believe that each campaign will use anything at its disposal to pressure news organizations for more favorable coverage.

The company pointed to comments made by Mr. Conde after the McDaniel imbroglio: “We will redouble our efforts to seek voices that represent different parts of the political spectrum.” It also shared data intended to show strong performance across its cable, broadcast and online operations.

The message was clear. Regardless of any turbulence, NBC has no plans to change course.

Jim Rutenberg is a writer at large for The Times and The New York Times Magazine and writes most often about media and politics. More about Jim Rutenberg

Michael M. Grynbaum writes about the intersection of media, politics and culture. He has been a media correspondent at The Times since 2016. More about Michael M. Grynbaum

## COMMENTS

The assignment problem is a fundamental combinatorial optimization problem. In its most general form, the problem is as follows: The problem instance has a number of agents and a number of tasks. Any agent can be assigned to perform any task, incurring some cost that may vary depending on the agent-task assignment.

How Edabit Works. This is an introduction to how challenges on Edabit work. In the Code tab above you'll see a starter function that looks like this: function hello () { } All you have to do is type return "hello edabit.com" between the curly braces { } and then click the Check button. If you did this correctly, the button will turn red and ...

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The problem is to find the assignment that minimizes the total cost or distance, taking into account both the distances and the flows. The distance matrix a. 11 min read. Job Assignment Problem using Branch And Bound. ... Transportation problem is a special kind of Linear Programming Problem (LPP) in which goods are transported from a set of ...

The problem is to assign each worker to at most one task, with no two workers performing the same task, while minimizing the total cost. Since there are more workers than tasks, one worker will not be assigned a task. MIP solution. The following sections describe how to solve the problem using the MPSolver wrapper. Import the libraries

Review the problem statement Each challenge has a problem statement that includes sample inputs and outputs. Some challenges include additional information to help you out. 2 of 6; Choose a language Select the language you wish to use to solve this challenge. 3 of 6; Enter your code

This repository contains all solutions for the course Algorithmic Toolbox offered on Coursera. The assignment solutions are in Python3. Disclaimer: The below solutions are for reference only.Please design and implement your own algorithms to pass the course.

In this step, we will solve the LP problem by calling solve () method. We can print the final value by using the following for loop. From the above results, we can infer that Worker-1 will be assigned to Job-1, Worker-2 will be assigned to job-3, Worker-3 will be assigned to Job-2, and Worker-4 will assign with job-4.

Equivalent Assignment Problem c(x, y) 00312 01015 43330 00110 12204 cp(x, y) 3891510 41071614 913111910 813122013 175119 8 13 11 19 13 5 4 3 0 8 9 + 8 - 13 10 Reduced costs. For x # X, y # Y, define cp(x, y) = p(x) + c(x, y) - p(y). Observation 1. Finding a min cost perfect matching with reduced costs

The problem set and programming assignment solutions are uploaded only for reference purposes. Please attempt the quizzes and programming assignments yourself and only look at the explanations if you find that you still can't understand it after consulting the discussion forums and reviewing the lecture content.

Step 1: Set up the cost matrix. The first step in solving the assignment problem is to set up the cost matrix, which represents the cost of assigning a task to an agent. The matrix should be square and have the same number of rows and columns as the number of tasks and agents, respectively.

Assignment Problem is a special type of linear programming problem where the objective is to minimise the cost or time of completing a number of jobs by a number of persons. The assignment problem in the general form can be stated as follows: "Given n facilities, n jobs and the effectiveness of each facility for each job, the problem is to ...

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The assignment problem represents a special case of linear programming problem used for allocating resources (mostly workforce) in an optimal way; it is a highly useful tool for operation and project managers for optimizing costs. The lpSolve R package allows us to solve LP assignment problems with just very few lines of code.

C is a general-purpose, imperative computer programming language, supporting structured programming, lexical variable scope and recursion, while a static type system prevents many unintended operations. C was originally developed by Dennis Ritchie between 1969 and 1973 at Bell Labs.

Assignment problem is a special type of linear programming problem which deals with the allocation of the various resources to the various activities on one to one basis. It does it in such a way that the cost or time involved in the process is minimum and profit or sale is maximum. Though there problems can be solved by simplex method or by ...

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There are two main conditions for applying Hungarian Method: (1) Square Matrix (n x n). (2) Problem should be of minimization type. Assignment model is a special application of Linear Programming Problem (LPP), in which the main objective is to assign the work or task to a group of individuals such that; i) There is only one assignment.

Solution 1: Brute Force. We generate n! possible job assignments and for each such assignment, we compute its total cost and return the less expensive assignment. Since the solution is a permutation of the n jobs, its complexity is O (n!). Solution 2: Hungarian Algorithm. The optimal assignment can be found using the Hungarian algorithm.

MSNBC placed a big bet on becoming comfort TV for liberals. Then it doubled down. Time slots on the cable network once devoted to news programming are now occupied by Trump-bashing opinion hosts.

The coordinate assignment process uses an approach inspired by graph drawing, that lays out the vertices (buses) and edges (transmission lines), formulated as a nonlinear programming problem with soft edge length constraints. The layout method is very computationally fast and scalable to large power system cases.