'Computational complexity' presentation slideshows

11/14/2003 10:00AM

11/14/2003 10:00AM. Valentin Polishchuk Preliminary Examination Geometric Motion Planning Problems with Applications Adviser: Professor Joseph Mitchell. Research Directions. Motion Planning Problems Geometric Algorithms for Air Traffic Management. Motion Planning Problems.

1.18k views • 104 slides

Parallel Programming in C with MPI and OpenMP

Parallel Programming in C with MPI and OpenMP. Michael J. Quinn. Chapter 12. Solving Linear Systems. Outline. Terminology Back substitution Gaussian elimination Jacobi method Conjugate gradient method. Terminology. System of linear equations Solve Ax = b for x Special matrices

752 views • 61 slides

Randomized Motion Planning

Randomized Motion Planning. Jean-Claude Latombe Computer Science Department Stanford University. Goal of Motion Planning. Answer queries about connectivity of a space Classical example: find a collision-free path in robot configuration space among static obstacles

641 views • 55 slides

New algorithms for Disjoint Paths and Routing Problems

639 views • 50 slides

First-order Logic

First-order Logic. Facts Objects relations. FOPC. Prob FOPC. Ontological commitment. Prob prop logic. Prop logic. facts. t/f/u. Deg belief. Epistemological commitment. Assertions; t/f. Expressiveness of Representations. Atomic ?Propositional?Relational?First order.

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Computationally-Efficient Approximation Mechanisms (cont.)

523 views • 40 slides

CELL B.E

CELL B.E.

490 views • 40 slides

Sorting and Searching Algorithms

Sorting and Searching Algorithms. Nikolay Kostov. Telerik Software Academy. academy.telerik.com. Senior Software Developer and Technical Trainer. http://Nikolay.IT. Table of Contents. Sorting Sorting and classification Review of the most popular sorting algorithms Searching

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Computational Complexity

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Manifold Learning on Probabilistic Graphical Models ?????????

Manifold Learning on Probabilistic Graphical Models ?????????. ??? : ??? ?? : ??? ?? & ??? ?? ???? CAD&CG ??????? 2010 ? 3 ? 5 ?. Outline. Background & Motivation Function Learning v.s. Statistical Modeling Manifold Regularized Variational Inference Algorithm Design & Examples

821 views • 29 slides

Hidden Variables as Fruitful Dead Ends

Hidden Variables as Fruitful Dead Ends. Scott Aaronson (MIT). WWJPD?.

371 views • 27 slides

Force Directed Algorithm

Force Directed Algorithm. Adel Alshayji 4/28/2005. What is FD Algorithm?. Force directed algorithms used to represents graphs. Force directed algorithms view the graph as a virtual physical system. The nodes of the graph are bodies of the system.

689 views • 25 slides

Active Sampling for Entity Matching

Active Sampling for Entity Matching. Aditya Parameswaran Stanford University Jointly with: Kedar Bellare , Suresh Iyengar , Vibhor Rastogi Yahoo! Research. Entity Matching. Goal : Find duplicate entities in a given data set Fundamental data cleaning primitive ? decades of prior work

430 views • 23 slides

Development of an Interdisciplinary fragment of the Russian Grid segment: state of the art

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An Evaluation of Parallelization Techniques for MRF Image Segmentation

An Evaluation of Parallelization Techniques for MRF Image Segmentation. Shane Mottishaw, Sergey Zhuravlev, Lisa Tang, Alexandra Fedorova, and Ghassan Hamarneh Presnted by: Ahmed Saad Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Canada. Motivation.

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Introduction to Computer Science Theory

Introduction to Computer Science Theory. How complex is a program (or algorithm)?. Algorithms is the study of What algorithms can be used to solve common problems? How fast/slow are those algorithms? For example: Binary search on an array of size m takes about log 2 ( m ) steps.

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Computational Complexity

Computational Complexity. Size Matters!. Suppose there are several algorithms which can all be used to perform the same task. We need some way to judge the efficiency of the algorithms against each other.

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The Web as a Parallel Corpus

The Web as a Parallel Corpus. A paper by Philip Resnik and Noah A. Smith ( 2003, Computational Linguistics ) My interpretation of their research. http://www.thebritishmuseum.ac.uk/compass/ixbin/goto?id=OBJ67. Contents:. Introduction to parallel corpora

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Solution methods for Discrete Optimization Problems

Solution methods for Discrete Optimization Problems. Computational complexity of discrete optimization problems. In terms of complexity of solution methods, discrete optimization problems can be divided into two classes :

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Relating computational and physical complexity

Relating computational and physical complexity. S.N. Coppersmith University of Wisconsin. Computational complexity: How the number of computational steps needed to solve a problem scales with problem size Physical complexity: No agreed-upon general definition

165 views • 9 slides