'Simulated annealing' diaporamas de présentation

Introduction to Data Assimilation

Introduction to Data Assimilation. Peter Jan van Leeuwen IMAU. Basic estimation theory. E{e 0 } = 0 E{e m } = 0 E{e 0 2 } = s 0 2 E{e m 2 } = s m 2 E{e 0 e m } = 0. T 0 = T + e 0. T m = T + e m. Assume a linear best estimate: T n = a T 0 + b T m with T n = T + e n

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Optimum design of optical filters and deposition monitoring methods

Optimum design of optical filters and deposition monitoring methods. Dimitris Kouzis - Loukas Supervisor : S . Maltezos Support : M. Fokitis. Contents. Multilayer Notch Optical Filters Applications Optimum design Deposition processes Monitoring techniques Conclusion and future work.

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Stochastic Molecular Replacement.

Stochastic Molecular Replacement. Nicholas M. Glykos MBG, DUTH, Alexandroupolis, Greece. 2x. 2x. Molecular Replacement as a rigid-body refinement problem :.

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Tabu Search for Model Selection in Multiple Regression

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Quantum algorithms with polynomial speedups

Quantum algorithms with polynomial speedups. Andris Ambainis University of Latvia. Search [Grover, 1996]. ?. ?. ?. ?. ?. . N objects; Find an object with a certain property. Conventional computer: N objects Quantum computer: ?N objects. Useful for many computational tasks.

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Recall: breadth-first search, step by step

Recall: breadth-first search, step by step. Implementation of search algorithms. Function General-Search(problem, Queuing-Fn) returns a solution, or failure nodes ? make-queue(make-node(initial-state[problem])) loop do if nodes is empty then return failure

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EL736 Communications Networks II: Design and Algorithms

EL736 Communications Networks II: Design and Algorithms. Class6: Optimization Methods (II) Yong Liu 10/17/2007. Outline. AMPL/CPLEX Package introduction example Stochastic Methods Local Search Simulated Annealing Evolutionary Algorithms Simulated Allocation.

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APPLICATION OF ORDER STATISTICS TO TERMINATION OF STOCHASTIC ALGORITHMS

APPLICATION OF ORDER STATISTICS TO TERMINATION OF STOCHASTIC ALGORITHMS. Vaida Bartkut? , Leonidas Sakalauskas. Outline. Introduction ; Application of order statistics to o ptimality testing and termination of the algorithm: S tochastic Approximation algorithms ;

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CSCI 5582 Artificial Intelligence

CSCI 5582 Artificial Intelligence. Lecture 5 Jim Martin. Today 9/12. Review informed searches Start on local, iterative improvement search. Review. How is the agenda ordered in the following searches? Uniform Cost Best First A* IDA*. Review: A * search.

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Informed Search Methods

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More on HW 2 (due Jan 26)

More on HW 2 (due Jan 26). Again, it must be in Python 2.7. For the A* algorithm, you will need an Open* list and a Closed list. States should have the coordinates of the point t he g-value cost of the path from init to here t he h-value estimate of cost to goal the parent state

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Outline

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VLSI Design

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Chapter 3 Heuristic Search Techniques

Chapter 3 Heuristic Search Techniques. Mwachaka, S. Contents. A framework for describing search methods is provided and several general purpose search techniques are discussed. All are varieties of Heuristic Search: Generate and test Hill Climbing Best First Search Problem Reduction

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Chapter 5 Advanced Search

Chapter 5 Advanced Search. Chapter 5 Contents. Constraint satisfaction problems Heuristic repair The eight queens problem Combinatorial optimization problems Local search Exchanging heuristics Iterated local search. Chapter 5 Contents, continued. Simulated annealing Genetic algorithms

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CSE 144 Project Part 2

CSE 144 Project Part 2. Overview. Goal: Implement a placement tool for standard cell design. Multiple rows Routing channel between rows Components of identical height but various width. Problems to be resolved. Assignment of components to rows

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?????????????????. ???????. ????????? 2005.12.10. ??????. Definition of Diffractive Optical Elements (DOE). ?????? ???? ???????????????????????????????????? ???????????? ?. Several ?m ~ several tens of?m. d =~ 1 m m. 1D or 2D ????????????. DOE ???????. Diffraction pattern. Period:

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Major Application: Finding Homologies

Major Application: Finding Homologies. (C) Mark Gerstein, Yale University bioinfo.mbb.yale.edu/mbb452a. AutoSimS. Local two-sequences alignment is the basis of sequence analysis, and perhaps the most widely used tool in computational molecular biology [1]

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Simulated Annealing

Simulated Annealing. Daniel Paden Tomasello. Simulated Annealing Algorithm. An approximation algorithm used for global optimization. In my model, simulated annealing is used to help a robot, which may only detect surrounding elevations, find the highest elevation. (Local Search)

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Snapshot of the simulated spin system during magnetization reversal. The reversal starts in

Kinetic Monte Carlo Simulations of Heat-assisted Magnetization Reversal in Ultrathin Films Per Arne Rikvold, Florida State University, DMR 0802288.

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