'Deterministic polynomial time' presentation slideshows

The Polynomial Hierarchy

The Polynomial Hierarchy. By Moti Meir And Yitzhak Sapir Based on notes from lectures by Oded Goldreich taken by Ronen Mizrahi, and lectures by Ely Porat. Introduction.

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P/Poly

761 views • 49 slides

FORMAL LANGUAGES, AUTOMATA AND COMPUTABILITY

15-453. FORMAL LANGUAGES, AUTOMATA AND COMPUTABILITY. Read sections 7.1 – 7.3 of the book for next time. TIME COMPLEXITY OF ALGORITHMS. (Chapter 7 in the textbook). COMPLEXITY THEORY. Studies what can and can’t be computed under limited resources such as time, space, etc.

557 views • 46 slides

Improved Derandomization of BPP using a hitting set generator Oded Goldreich Avi Wigderson

Improved Derandomization of BPP using a hitting set generator Oded Goldreich Avi Wigderson. D91922010( ??? ),R91922045( ??? ),R91922046( ??? ) National Taiwan University Department of Computer Science and Information Engineering 05/14/2004. Outline. Introduction to Hitting-set generator.

477 views • 35 slides

Time Complexity

Time Complexity. Consider a deterministic Turing Machine which decides a language. For any string the computation of terminates in a finite amount of transitions. Initial state. Accept or Reject. Decision Time = #transitions. Initial state. Accept or Reject.

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Time Complexity

Time Complexity. Consider a deterministic Turing Machine which decides a language. For any string the computation of terminates in a finite amount of transitions. Initial state. Accept or Reject. Decision Time = #transitions. Initial state. Accept or Reject.

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Time Complexity

Time Complexity. We use a multitape Turing machine We count the number of steps until a string is accepted We use the O(k) notation. Example:. Algorithm to accept a string :. Use a two-tape Turing machine Copy the on the second tape Compare the and. Time needed:.

418 views • 33 slides

Time Complexity

Time Complexity. We use a multitape Turing machine We count the number of steps until a string is accepted We use the O(k) notation. Example:. Algorithm to accept a string :. Use a two-tape Turing machine Copy the on the second tape Compare the and. Time needed:.

340 views • 33 slides

NP-COMPLETE - Department of Information Technology

A presentation on NP-COMPLETE and is presented by Prof. Manjusha Amritkar, from the department of Information Technology at International Institute of Information Technology, Iu00b2IT. The presentation includes topics like The Traveling Salesman Problem, The Bin Packing Problem, Time Complexity of Problems, Quantifying Easy-to-compute and more.

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Chapter 1: Introduction

Chapter 1: Introduction. What is the course all about? Problems, Instances and Algorithms Running Time v.s. Computational Complexity. What is this Course About?. Particular Topics: NP-completeness NP-hardness Heuristics PSPACE-completeness The polynomial hierarchy, etc. Generally:

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Polynomial Time Reductions

Polynomial Time Reductions. Polynomial Computable function :. For any computes in polynomial time. Language A is polynomial time reducible to language B if there is a polynomial computable function such that:. Theorem:. Suppose that is polynomial reducible to .

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Space and time constructible functions.

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P, NP, and Exponential Problems

224 views • 10 slides