'Polynomial time reduction' diaporamas de présentation

NPC

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Sarah Saneei Shahed University

NP-Completeness. Dr. Seyed Hamid Haji Seyed Javadi. Sarah Saneei Shahed University. Overview. Preface examples NP-completeness and the class P and NP Overview of showing problems to be NP-complete Polynomial time

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Chapter 8

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NP-complete Languages

NP-complete Languages. Polynomial Time Reductions. Polynomial Computable function :. There is a deterministic Turing machine. such that for any string computes in polynomial time:. Observation:. since, cannot use more than tape space in time. Definition:.

536 views • 51 slides

NP-complete Languages

NP-complete Languages. Polynomial Time Reductions. Polynomial Computable function :. There is a deterministic Turing machine. such that for any string computes in polynomial time:. Observation:. since, cannot use more than tape space in time. Definition:.

619 views • 50 slides

NP-complete Languages

NP-complete Languages. Polynomial Time Reductions. Polynomial Computable function :. There is a deterministic Turing machine. such that for any string computes in polynomial time:. Observation:. the length of is bounded. since, cannot use

516 views • 50 slides

The Complexity of the Network Design Problem

The Complexity of the Network Design Problem. Networks, 1978 Classic Paper Reading 99.12. Outline. Introduction NDP is NP-complete SNDP is NP-complete Conclusion. Introduction. B96902094 ???.

529 views • 43 slides

The Complexity of the Network Design Problem

The Complexity of the Network Design Problem. Networks, 1978 Classic Paper Reading 99.12. Outline. Introduction NDP is NP-complete SNDP is NP-complete Conclusion. Introduction. B96902094 ???.

432 views • 43 slides

Lectures on NP-hard problems and Approximation algorithms

Lectures on NP-hard problems and Approximation algorithms. COMP 523: Advanced Algorithmic Techniques Lecturer: Dariusz Kowalski. Overview. Previous lectures: Greedy algorithms Dynamic programming Network flows These lectures: NP-hard problems Approximation algorithms. P versus NP.

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NP-Completeness

NP-Completeness. Lecture for CS 302. Traveling Salesperson Problem. You have to visit n cities You want to make the shortest trip How could you do this? What if you had a machine that could guess?. Non-deterministic polynomial time.

517 views • 30 slides

NP-Completeness

NP-Completeness. Lecture for CS 302. Traveling Salesperson Problem. You have to visit n cities You want to make the shortest trip How could you do this? What if you had a machine that could guess?. Non-deterministic polynomial time.

427 views • 30 slides

Everything you always wanted to know about theoretical computer science (but were afraid to ask)

Everything you always wanted to know about theoretical computer science (but were afraid to ask). Dimitris Achlioptas Microsoft Research. Turing’s Machine. Alphabet Σ , state space K f : K × Σ → K × Σ × {←,→, ? } × { Halt,Yes,No} Language: L µ Σ * is decided by M L

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Everything you always wanted to know about theoretical computer science (but were afraid to ask)

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On worst-case to average-case reductions for NP

On worst-case to average-case reductions for NP. Danny Gutfreund (Harvard) Ronen Shaltiel (Haifa U.) and Amnon Ta-Shma (Tel-Aviv U.). Negative results. Thm: [BT,FF] If PH does not collapse, then there is no non-adaptive reduction from solving SAT

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CMPUT 671 Hard Problems

CMPUT 671 Hard Problems. Winter 2002 Joseph Culberson Home Page. What are you doing?. What are you doing?. What am I doing?. What is Hard?. Problems and Complexity P, NP, PSPACE; between and beyond Worst case, Randomized, Average case. What are you doing?. What am I doing?.

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Additional NP-complete problems

Giorgi Japaridze Theory of Computability. Additional NP-complete problems. Section 7.5. 7.5.a. Giorgi Japaridze Theory of Computability. Why we want to see more NP-completeness proofs. For reasons that are not well understood, most naturally occurring

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Computability and Complexity

Computability and Complexity. 20-1. Class NL. Computability and Complexity Andrei Bulatov. Computability and Complexity. 20-2. Reducing Problems.

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