'Greedy choice' presentation slideshows

Chapter 5

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Fundamental Techniques

Fundamental Techniques. CS 5050 Chapter 5 Goal: review complexity analysis Talk of categories used to describe algorithms. Algorithmic Frameworks. Greedy Divide-and-conquer Discard-and-conquer Top-down Refinement Dynamic Programming Backtracking/Brute Force Branch-and-bound

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CSE 3101

CSE 3101. Prof. Andy Mirzaian. Greedy Algorithms. STUDY MATERIAL:. [CLRS] chapter 16 Lecture Notes 7, 8. TOPICS. Combinatorial Optimization Problems The Greedy Method Problems: Coin Change Making Event Scheduling Interval Point Cover

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COSC 3101A - Design and Analysis of Algorithms 9

COSC 3101A - Design and Analysis of Algorithms 9. Knapsack Problem Huffman Codes Introduction to Graphs. Many of these slides are taken from Monica Nicolescu, Univ. of Nevada, Reno, monica@cs.unr.edu. The Knapsack Problem. The 0-1 knapsack problem

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Greedy Algorithms

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Greedy Algorithms

Greedy Algorithms. 15-211 Fundamental Data Structures and Algorithms. Peter Lee March 19, 2004. Announcements. HW6 is due on April 5! Quiz #2 postponed until March 31 an online quiz requires up to one hour of uninterrupted time with a web browser

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Greedy Algorithms

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Greedy Algorithms and Matroids

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Greedy Algorithm

Greedy Algorithm. A greedy algorithm always makes the choice that looks best at the moment Key point: Greed makes a locally optimal choice in the hope that this choice will lead to a globally optimal solution

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Greedy Algorithms

Greedy Algorithms. Overview. Like dynamic programming, used to solve optimization problems. Problems exhibit optimal substructure (like DP). Problems also exhibit the greedy-choice property. When we have a choice to make, make the one that looks best right now .

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A Note on Useful Algorithmic Strategies

A Note on Useful Algorithmic Strategies. Kun-Mao Chao ( ??? ) Department of Computer Science and Information Engineering National Taiwan University, Taiwan WWW: http://www.csie.ntu.edu.tw/~kmchao. Greedy Algorithm. A greedy method always makes a locally optimal (greedy) choice.

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A Note on Useful Algorithmic Strategies

A Note on Useful Algorithmic Strategies. Kun-Mao Chao ( ??? ) Department of Computer Science and Information Engineering National Taiwan University, Taiwan WWW: http://www.csie.ntu.edu.tw/~kmchao. Greedy Algorithm. A greedy method always makes a locally optimal (greedy) choice.

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Design and Analysis of Algorithms Greedy algorithms, coin changing problem

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Advanced Algorithms

Greedy algorithm Job-select problem Greedy vs DP. Advanced Algorithms. Learning outcomes. Able to write greedy algorithms for the problems discussed in the class able to analyse the complexity of the algorithms. Greedy Approach. Also for optimization problem

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Advanced Algorithms

Greedy algorithm Job-select problem Greedy vs DP. Advanced Algorithms. Learning outcomes. Able to write greedy algorithms for the problems discussed in the class able to analyse the complexity of the algorithms. Greedy Approach. Also for optimization problem

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Design Methods for Algorithms

Design Methods for Algorithms. Andreas Klappenecker. Design Methods. We have discussed examples of the following algorithm design principles: Dynamic Programming Paradigm Greedy Paradigm Divide-and-Conquer Paradigm. Main Question.

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Greedy Algorithms

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Lecture 6

Lecture 6. Greedy Algorithm. Topics. Reference: Introduction to Algorithm by Cormen Chapter 17: Greedy Algorithm. Greedy Algorithm. Algorithm for optimization problems typically go through a sequence of steps, with a set of choices at each step.

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Query Result Caching

Query Result Caching. Prasan Roy, Krithi Ramamritham, S. Seshadri, S. Sudarshan Pradeep Shenoy, Jinesh Vora. Model. Predictive Caching - use history Query results/ intermediate Single user stream - very similar queries Global sequence of queries - long term patterns

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Greedy Algorithms

Greedy Algorithms. Greedy Algorithm Design. Comparison:. Dynamic Programming. Greedy Algorithms. At each step, the choice is determined based on solutions of subproblems. At each step, we quickly make a choice that currently looks best. --A local optimal (greedy) choice.

351 views • 8 slides