'Fractional knapsack problem' presentation slideshows

Optimal Allocation of Filters against DDoS Attacks

Optimal Allocation of Filters against DDoS Attacks. Advisor: Frank, Y.S. Lin Present by C.Y. Hsu. Optimal Allocation of Filters against DDoS Attacks. Author: El Defrawy , K.; Markopoulou , A.; Argyraki , K. Univ. of California, Irvine

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Implementing a Graph

Implementing a Graph. Implement a graph in three ways: Adjacency List Adjacency-Matrix Pointers/memory for each node (actually a form of adjacency list). Adjacency List. List of pointers for each vertex. Undirected Adjacency List. Adjacency List.

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Greedy Algorithms CSc 4520/6520

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RAIK 283 Data Structures & Algorithms

RAIK 283 Data Structures & Algorithms. 0-1 Knapsack problem Dr. Ying Lu ylu@cse.unl.edu. RAIK 283 Data Structures & Algorithms. Giving credit where credit is due: Most of slides for this lecture are based on slides created by Dr. David Luebke, University of Virginia.

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Greedy Algorithms Spanning Trees

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CS 332 - Algorithms Dynamic programming 0-1 Knapsack problem

CS 332 - Algorithms Dynamic programming 0-1 Knapsack problem. Review: Dynamic programming. DP is a method for solving certain kind of problems DP can be applied when the solution of a problem includes solutions to subproblems We need to find a recursive formula for the solution

1.19k views • 39 slides

Analysis of Algorithms CS 477/677

Analysis of Algorithms CS 477/677. Instructor: Monica Nicolescu Lecture 17. Greedy Algorithms. Similar to dynamic programming, but simpler approach Also used for optimization problems Idea: When we have a choice to make, make the one that looks best right now

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ITCS 6114 Dynamic programming 0-1 Knapsack problem

ITCS 6114 Dynamic programming 0-1 Knapsack problem. 0-1 Knapsack problem. Given a knapsack with maximum capacity W , and a set S consisting of n items Each item i has some weight w i and benefit value b i (all w i , b i and W are integer values)

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Fundamental Techniques

Fundamental Techniques. There are some algorithmic tools that are quite specialised . They are good for problems they are intended to solve, but they are not very versatile .

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Greedy Algorithms:

Greedy Algorithms:. A greedy algorithm always makes the choice that looks best at the moment. It makes a local optimal choice in the hope that this choice will lead to a globally optimal solution. Greedy algorithms yield optimal solutions for many (but not all) problems. .

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CS 332: Algorithms

CS 332: Algorithms. NP Completeness. Administrivia. Homework 5 clarifications: Deadline extended to Thursday Dec 7 However, no late assignments will be accepted This is so that I can discuss it in class You may not work with others on this one. Review: Dynamic Programming.

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CS 332: Algorithms

CS 332: Algorithms. Greedy Algorithms Continued. Administrivia. Hand in Homework 4 Hand back remaining midterms. Review: LCS Via Dynamic Programming . Longest common subsequence ( LCS ) problem: Given two sequences x[1..m] and y[1..n], find the longest subsequence which occurs in both

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Knapsack problem

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Algorithm Design approaches

Algorithm Design approaches. Dr. Jey Veerasamy. Petrol cost minimization problem. You need to go from S to T by car, spending the minimum for petrol. S. T. Quick-sort . Merge-sort. Divide & Conquer - characteristics. # of stops minimization problem.

247 views • 14 slides

The Greedy Method

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CSCI 6212 Design and Analysis of Algorithms The Greedy Method

CSCI 6212 Design and Analysis of Algorithms The Greedy Method. Dr. Juman Byun The George Washington University. Please drop this course if you have not taken the following prerequisite. Sometimes enthusiasm alone is not enough. CSci 1311: Discrete Structures I (3)

251 views • 7 slides