'Approximation algorithm' diaporamas de présentation

Lecture 1: Symmetric Traveling Salesman Problem

Lecture 1: Symmetric Traveling Salesman Problem. Ola Svensson. Georgia Tech 2019. Who?. LPs. Exercises. APPROX. ATSP. TSP. WHO am I and What will I talk about?. About Me. Ola Svensson ola.svensson@epfl.ch http://theory.epfl.ch/osven Faculty at EPFL Research on algorithms

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The Internal Steiner Tree Problem: Hardness and Approximations

The Internal Steiner Tree Problem: Hardness and Approximations. Sun-Yuan Hsieh ( ??? ) Department of Computer Science and Information Engineering Institute of Medical Informatics Institute of Manufacturing Information and Systems National Cheng Kung University hsiehsy@mail.ncku.edu.tw

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OUTSTANDING PROBLEMS IN GEOMETRIC CONSTRAINT SOLVING FOR CAD

OUTSTANDING PROBLEMS IN GEOMETRIC CONSTRAINT SOLVING FOR CAD. Meera Sitharam, University of Florida Partially supported by NSF grants CCR 99-02025, EIA 00-96104. ORGANIZATION. CAD motivation and state of the art Suite of Formal Problems Our contribution-- FRONTIER

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Approximation Algorithms

Approximation Algorithms. Chapter 1- Introduction. Why Approximation Algorithms. Problems that we cannot find an optimal solution in a polynomial time Eg: Set Cover, Bin Packing Need to find a near-optimal solution: Heuristic Approximation algorithms:

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Local Search

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Local Search

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Practical Approximation Algorithms for Separable Packing LPs

Practical Approximation Algorithms for Separable Packing LPs. F.F. Dragan (Kent State) A.B. Kahng (UCSD) I. Mandoiu (UCLA/UCSD) S. Muddu (Sanera Systems) A. Zelikovsky (Georgia State). Outline. VLSI design motivation Global routing via buffer-blocks Separable packing ILP formulations

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Approximation Algorithms for Stochastic Optimization

Approximation Algorithms for Stochastic Optimization. Chaitanya Swamy Caltech and U. Waterloo Joint work with David Shmoys Cornell University. Stochastic Optimization. Way of modeling uncertainty .

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CMSC 471 Fall 2002

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Network Flow Spanners

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Approximation Algorithms: Job Scheduling

Approximation Algorithms: Job Scheduling. Lecture 16: March 15. Job Scheduling: General Setting. There are n jobs , each job has: a processing time p(i) (the time to finish this job) a release time r(i) (can only process this job after time r(i))

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An FPTAS for #Knapsack and Related Counting Problems

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Sampling-based Approximation Algorithms for Multi-stage Stochastic Optimization

Sampling-based Approximation Algorithms for Multi-stage Stochastic Optimization. Chaitanya Swamy Caltech and U. Waterloo Joint work with David Shmoys Cornell University. Stochastic Optimization. Way of modeling uncertainty .

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Approximation Algorithm for Multicut

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Test Case Filtering and Prioritization Based on Coverage of Combinations of Program Elements

Test Case Filtering and Prioritization Based on Coverage of Combinations of Program Elements. Wes Masri and Marwa El- Ghali American Univ. of Beirut ECE Department Beirut, Lebanon wm13@aub.edu.lb. Test Case Filtering.

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336C, March 7 2012

336C, March 7 2012. Tandy Warnow. Topics for today. Reviewing Exam 2 Writing mathematics Graph theory. Graph Theory. Vertex Cover : a subset X of vertices so that every edge has at least one of its endpoints in X Clique : a subset X of vertices that are pairwise adjacent

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Proximity algorithms for nearly-doubling spaces

Proximity algorithms for nearly-doubling spaces. Lee-Ad Gottlieb Robert Krauthgamer Weizmann Institute. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A. Proximity problems. In arbitrary metric space, some proximity problems are hard

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RAPID: Ra ndomized P harmacophore I dentification for D rug Design

RAPID: Ra ndomized P harmacophore I dentification for D rug Design. PW Finn, LE Kavraki, JC Latombe, R Motwani, C Shelton, S Venkatasubramanian, A Yao. Presented by Greg Goldgof. Key Terms.

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Network Optimization Problems: Models and Algorithms

Network Optimization Problems: Models and Algorithms. In this handout: Approximation Algorithms Traveling Salesman Problem. Classes of discrete optimization problems. Class 1 problems have polynomial-time algorithms for solving the problems optimally. Ex .: Min. Spanning Tree problem

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