'Polynomial time algorithm' presentation slideshows

Approximation Algorithms

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The Theory of NP-Completeness

The Theory of NP-Completeness. Review: Finding lower bound by problem transformation. Problem X reduces to problem Y (X ?Y ) iff X can be solved by using any algorithm which solves Y. If X ?Y , Y is more difficult.

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Exact Algorithms for Hard Problems

Exact Algorithms for Hard Problems. An introduction. This talk. Intro Exact algorithms Fixed parameter algorithms. Why?. Practical problems often NP-hard Must still be solved, so use slow exact algorithm Question: how fast can we do it? Here: analysis of worst case times

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Design and Analysis of Computer Algorithm Lecture 10

Design and Analysis of Computer Algorithm Lecture 10. Pradondet Nilagupta Department of Computer Engineering. Acknowledgement.

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New algorithms for Disjoint Paths and Routing Problems

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The Theory of NP-Completeness

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Interval, circle graphs and circle graph recognition using split decomposition

Interval, circle graphs and circle graph recognition using split decomposition. Presented by Steven Correia Kent state university Nov-18-2011 Email: scorreia@kent.edu. Based on :[1][4]. Background and motivation. Can highly connectivity of protein in cells be found easily?

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Shortest Vector In A Lattice is NP-Hard to approximate

Shortest Vector In A Lattice is NP-Hard to approximate . Daniele Micciancio. Speaker: Asaf Weiss. Definitions. A Lattice in : All integer combinations of given linearly independent vectors: The vectors are called the Lattice Basis . The integer n is called the Lattice Rank .

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The NP class. NP-completeness

The NP class. NP-completeness. The NP-class. The NP class is a class that contains all the problems that can be decided by a Non-Deterministic Turing Machine in polynomial time. In other words, the longest accepting path of the machine is going to be polynomial in terms of the size of the input.

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Chapter 4. Duality Theory

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The NP class. NP-completeness

The NP class. NP-completeness. The NP-class.

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6.006- Introduction to Algorithms

6.006- Introduction to Algorithms. Lecture 24 NP-completeness (The Dismal Computer Science). Prof . Constantinos Daskalakis. Tractable Problems. We have seen many problems that can be solved in polynomial time . e.g. finding the shortest path in a graph e.g.2 sorting n numbers

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Lecture 14

Lecture 14. Learning Inductive inference Probably approximately correct learning. What is learning?. Key point: all learning can be seen as learning the representation of a function. Will become clearer with more examples! Example representations: propositional if-then rules

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Matchings and where they lead us

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Pseudo-polynomial time algorithm (The concept and the terminology are important)

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Integrating Logic Retiming and Register Placement

Integrating Logic Retiming and Register Placement. Tzu-Chieh Tien, Hsiao-Pin Su, Yu-Wen Tsay Yih-Chih Chou, and Youn-Long Lin Department of Computer Science Tsing Hua University Hsin-Chu, Taiwan, R.O.C. Outline. Motivation Key problems Proposed System flow Retiming algorithm

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Public Key Cryptography

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Fine-Grain Sparse Matrix Partitioning with Constraints

Fine-Grain Sparse Matrix Partitioning with Constraints. Erik Boman Sandia National Labs, Albuquerque, NM Umit Catalyurek Ohio State University. Outline. Load-balancing & partitioning Sparse matrix-vector multiplication Models: bipartite graph, hypergraph 1d and 2d distributions

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Complexity Theory

Complexity Theory. A light introduction to a rather important and complex topic. October 12, 2007 Presented by Sean Szumlanski. Programme. Graph coloring: a practical application This problem will serve as our touchstone throughout the

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Introduction to Randomized Algorithms and the Probabilistic Method

Introduction to Randomized Algorithms and the Probabilistic Method. Lecture 20: April 12. Making Decision. Flip a coin. Making Decision. Flip a coin!. An algorithm which flip coins is called a randomized algorithm. Why Randomness?. Making decisions could be complicated.

414 views • 17 slides