# Programming Assignment 1: Flows in Networks solution

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## Description

Learning Outcomes Upon completing this programming assignment you will be able to: 1. Apply Ford-Fulkerson and/or Edmonds-Karp algorithm to solve various computational problems efficiently. This will typically require you to invent a way to reduce the problem to a maximum flow or maximum matching problem and then use an efficient algorithm to solve it. 2. Design and implement efficient algorithms for the following computational problems: (a) evacuating people from the city as fast as possible; (b) assigning airline crews to the aircrafts efficiently; (c) visualizing stock price data compactly.
Passing Criteria: 2 out of 3 Passing thisprogramming assignmentrequires passingat least2out of3code problemsfrom thisassignment. In turn, passing a code problem requires implementing a solution that passes all the tests for this problem in the grader and does so under the time and memory limits specified in the problem statement.
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Contents 1 Problem: Evacuating People 3
2 Problem: Assigning Airline Crews to Flights 6
3 Advanced Problem: Stock Charts 8
4 General Instructions and Recommendations on Solving Algorithmic Problems 11 4.1 Reading the Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2 Designing an Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.3 Implementing Your Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.4 Compiling Your Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.5 Testing Your Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.6 Submitting Your Program to the Grading System . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.7 Debugging and Stress Testing Your Program . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5 Frequently Asked Questions 14 5.1 I submit the program, but nothing happens. Why? . . . . . . . . . . . . . . . . . . . . . . . . 14 5.2 I submit the solution only for one problem, but all the problems in the assignment are graded. Why? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 5.3 What are the possible grading outcomes, and how to read them? . . . . . . . . . . . . . . . . 14 5.4 How to understand why my program fails and to fix it? . . . . . . . . . . . . . . . . . . . . . 15 5.5 Why do you hide the test on which my program fails? . . . . . . . . . . . . . . . . . . . . . . 15 5.6 My solution does not pass the tests? May I post it in the forum and ask for a help? . . . . . 16 5.7 My implementation always fails in the grader, though I already tested and stress tested it a lot. Would not it be better if you give me a solution to this problem or at least the test cases that you use? I will then be able to fix my code and will learn how to avoid making mistakes. Otherwise, I do not feel that I learn anything from solving this problem. I am just stuck. . . 16
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1 Problem: Evacuating People Problem Introduction
In this problem, you will apply an algorithm for finding maximum flow in a network to determine how fast people can be evacuated from the given city.
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Memory Limit. 512Mb. Sample 1. Input: 5 7 1 2 2 2 5 5 1 3 6 3 4 2 4 5 1 3 2 3 2 4 1 Output: 6 Explanation: In this sample, the road graph with capacities looks like this:
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We can evacuate 2 people through the route 1−2−5, additional 3 people through the route 1−3−2−5 and 1 more person through the route 1−3−4−5 — for a total of 6 people. It is impossible to evacuate more people each hour, as the total capacity of all roads incoming to the capital city 5 is 6 people per hour. Sample 2. Input: 4 5 1 2 10000 1 3 10000 2 3 1 3 4 10000 2 4 10000 Output: 20000
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Explanation: In this sample, the road graph with capacities looks like this:
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We can evacuate 10000 people through the route 1−2−4 and additional 10000 people through the route 1−3−4 totalling in 20000 people per hour. It is impossible to evacuate more people each hour, as the total capacity of the roads outgoing from the city number 1 is 20000 people per hour. Pay attention to this example if you think of using a simple Ford–Fulkerson algorithm. Note how it works on such graph, and why it may be a bad idea to use this algorithm on big networks with large capacities.
Starter Files The starter solutions for this problem read the data from the input, build a graph data structure optimized for finding maximum flow in the graph, pass it to a blank procedure for finding the maximum flow and output the result. You need to implement this procedure if you are using C++, Java, or Python3. For other programming languages, you need to implement a solution from scratch. Filename: evacuation
What To Do Implement an algorithm for finding maximum flow described in the lectures, but be careful with the choice of the algorithm, see the comments for the second example from the problem statement.
Need Help? Ask a question or see the questions asked by other learners at this forum thread.
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2 Problem: Assigning Airline Crews to Flights Problem Introduction
Inthisproblem, youwillapplyanalgorithmforfindingmaximummatching in a bipartite graph to assign airline crews to flights in the most efficient way.
Problem Description Task. The airline offers a bunch of flights and has a set of crews that can work on those flights. However, the flights are starting in different cities and at different times, so only some of the crews are able to work on a particular flight. You are given the pairs of flights and associated crews that can work on those flights. You need to assign crews to as many flights as possible and output all the assignments. Input Format. Thefirstlineoftheinputcontainsintegers n and m —thenumberofflightsandthenumber of crews respectively. Each of the next n lines contains m binary integers (0 or 1). If the j-th integer in the i-th line is 1, then the crew number j can work on the flight number i, and if it is 0, then it cannot. Constraints. 1 ≤ n,m ≤ 100. Output Format. Output n integers — for each flight, output the 1-based index of the crew assigned to this flight. If no crew is assigned to a flight, output −1 as the index of the crew corresponding to it. All the positive indices in the output must be between 1 and m, and they must be pairwise different, but you can output any number of −1’s. If there are several assignments with the maximum possible number of flights having a crew assigned, output any of them. Time Limits. language C C++ Java Python C# Haskell JavaScript Ruby Scala time in seconds 1 1 1.5 5 1.5 2 5 5 3
Memory Limit. 512Mb. Sample 1. Input: 3 4 1 1 0 1 0 1 0 0 0 0 0 0 Output: 1 2 -1
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Explanation: In this sample, the bipartite graph of flights (on the left) and crews (on the right) looks like this:
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We can assign first crew to the first flight and second crew to the second flight, and no crews can work on the third flight, so this is an optimal assignment. Sample 2. Input: 2 2 1 1 1 0 Output: 2 1
Explanation: In this sample, the bipartite graph of flights (on the left) and crews (on the right) looks like this:
f1
f2
c1
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If we assign the first crew to the first flight, we won’t be able to assign any crew to the second flight. It is optimal to assign the second crew to the first flight and the first crew to the second flight, because this way we have a crew assigned to each flight.
Starter Files The starter solutions for this problem read the data from the input, pass it to a blank procedure that implements an incorrect greedy algorithm for finding the maximum matching, and output the result. You need to change this procedure to implement a correct algorithm for finding the maximum matching if you are using C++, Java, or Python3. For other programming languages, you need to implement a solution from scratch. Filename: airline_crews
What To Do Implement an algorithm for finding the maximum matching in the bipartite graph that was described in the lectures.
Need Help? Ask a question or see the questions asked by other learners at this forum thread.
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3 Advanced Problem: Stock Charts Westronglyrecommendyoustartsolvingadvancedproblemsonlywhenyouaredonewiththebasicproblems (for some advanced problems, algorithms are not covered in the video lectures and require additional ideas to be solved; for some other advanced problems, algorithms are covered in the lectures, but implementing them is a more challenging task than for other problems).
Problem Introduction We would like to thank the organizers of Google Code Jam — the annual worldwide programming competition held by Google — for allowing us to use this problem from one of the past rounds. Yes, that’s right: you have a problem from a world championship in programming in your first homework, but hey, this is the fifth course in the Data Structures and Algorithms Specialization, and it has the words “Advanced Algorithms” in its name for a reason 🙂 You’ve made it thus far — you can do this.
In this problem you will need to guess how to apply the algorithms from this module to find the most compact way of visualizing stock price data using charts.
Problem Description Task. You’re in the middle of writing your newspaper’s end-of-year economics summary, and you’ve decided that you want to show a number of charts to demonstrate how different stocks have performed over the course of the last year. You’ve already decided that you want to show the price of n different stocks, all at the same k points of the year. A simple chart of one stock’s price would draw lines between the points (0,price0),(1,price1),…,(k− 1,pricek−1), where pricei is the price of the stock at the i-th point in time. In order to save space, you have invented the concept of an overlaid chart. An overlaid chart is the combination of one or more simple charts, and shows the prices of multiple stocks (simply drawing a line for each one). In order to avoid confusion between the stocks shown in a chart, the lines in an overlaid chart may not cross or touch. Given a list of n stocks’ prices at each of k time points, determine the minimum number of overlaid charts you need to show all of the stocks’ prices. Input Format. The first line of the input contains two integers n and k — the number of stocks and the number of points in the year which are common for all of them. Each of the next n lines contains k integers. The i-th of those n lines contains the prices of the i-th stock at the corresponding k points in the year. Constraints. 1 ≤ n ≤ 100; 1 ≤ k ≤ 25. All the stock prices are between 0 and 1 000 000. Output Format. Output a single integer — the minimum number of overlaid charts to visualize all the stock price data you have. Time Limits. language C C++ Java Python C# Haskell JavaScript Ruby Scala time in seconds 2 2 3 10 3 4 10 10 6
Memory Limit. 512Mb.
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Sample 1. Input: 3 4 1 2 3 4 2 3 4 6 6 5 4 3 Output: 2 Explanation: This data can be put into two following overlaid charts:
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However, we cannot put all the data in one overlaid chart, as the lines corresponding to the third stock would touch the lines corresponding to the second stock, because they have the same price value at the third point. Sample 2. Input: 3 3 5 5 5 4 4 6 4 5 4 Output: 3
Explanation: Each stock can be put on its own overlaid stock chart, of course. But no two stocks can be put on the same overlaid stock chart: first and second would intersect between points 2 and 3, first and third would touch in point 2, second and third would touch in point 1.
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Starter Files The starter solutions for this problem read the data from the input, pass it to a procedure and output the result. This procedure tries to pack the stock price data into the minimum possible number of overlaid charts using a greedy algorithm, but it sometimes fails. You need to implement another algorithm in this procedure if you are using C++, Java, or Python3. For other programming languages, you need to implement a solution from scratch. Filename: stock_charts
What To Do Determine what are the conditions under which two stocks can be put on the same chart. Then think when more than 2 stocks can be put on the same chart. Try to reduce the problem to the maximum matching in a bipartite graph problem, and to the first trick on the way is to create a bipartite graph of stocks with two nodes for each stock.
Need Help? Ask a question or see the questions asked by other learners at this forum thread.
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4 General Instructions and Recommendations on Solving Algorithmic Problems Your main goal in an algorithmic problem is to implement a program that solves a given computational problem in just few seconds even on massive datasets. Your program should read a dataset from the standard input and write an answer to the standard output. Below we provide general instructions and recommendations on solving such problems. Before reading them, go through readings and screencasts in the first module that show a step by step process of solving two algorithmic problems: link.
4.1 Reading the Problem Statement You start by reading the problem statement that contains the description of a particular computational task as well as time and memory limits your solution should fit in, and one or two sample tests. In some problems your goal is just to implement carefully an algorithm covered in the lectures, while in some other problems you first need to come up with an algorithm yourself.
4.2 Designing an Algorithm If your goal is to design an algorithm yourself, one of the things it is important to realize is the expected running time of your algorithm. Usually, you can guess it from the problem statement (specifically, from the subsection called constraints) as follows. Modern computers perform roughly 108–109 operations per second. So, if the maximum size of a dataset in the problem description is n = 105, then most probably an algorithm with quadratic running time is not going to fit into time limit (since for n = 105, n2 = 1010) while a solution with running time O(nlogn) will fit. However, an O(n2) solution will fit if n is up to 103 = 1000, and if n is at most 100, even O(n3) solutions will fit. In some cases, the problem is so hard that we do not know a polynomial solution. But for n up to 18, a solution with O(2nn2) running time will probably fit into the time limit. To design an algorithm with the expected running time, you will of course need to use the ideas covered in the lectures. Also, make sure to carefully go through sample tests in the problem description.
4.3 Implementing Your Algorithm When you have an algorithm in mind, you start implementing it. Currently, you can use the following programming languages to implement a solution to a problem: C, C++, C#, Haskell, Java, JavaScript, Python2, Python3, Ruby, Scala. For all problems, we will be providing starter solutions for C++, Java, and Python3. If you are going to use one of these programming languages, use these starter files. For other programming languages, you need to implement a solution from scratch.
4.4 Compiling Your Program For solving programming assignments, you can use any of the following programming languages: C, C++, C#, Haskell, Java, JavaScript, Python2, Python3, Ruby, and Scala. However, we will only be providing starter solution files for C++, Java, and Python3. The programming language of your submission is detected automatically, based on the extension of your submission. We have reference solutions in C++, Java and Python3 which solve the problem correctly under the given restrictions, and in most cases spend at most 1/3 of the time limit and at most 1/2 of the memory limit. You can also use other languages, and we’ve estimated the time limit multipliers for them, however, we have no guarantee that a correct solution for a particular problem running under the given time and memory constraints exists in any of those other languages. Your solution will be compiled as follows. We recommend that when testing your solution locally, you use the same compiler flags for compiling. This will increase the chances that your program behaves in the
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same way on your machine and on the testing machine (note that a buggy program may behave differently when compiled by different compilers, or even by the same compiler with different flags). ∙ C (gcc 5.2.1). File extensions: .c. Flags: gcc -pipe -O2 -std=c11