'Constraint graph' diaporamas de présentation

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Constraint Satisfaction

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C ONSTRAINT Networks Chapters 1-2

C ONSTRAINT Networks Chapters 1-2. Compsci-275 Spring 2009. Class Information. Instructor: Rina Dechter Days: Monday & Wednesday Time: 11:00 - 12:20 pm 11:00 - 12:20 pm Class page: http://www.ics.uci.edu/~ dechter/ics-275a/spring-2009 /. Text book (required). Rina Dechter,

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LINEAR PROGRAMMING

LINEAR PROGRAMMING. FORMULATION AND GRAPHIC METHOD. DEFINITION. Mathematical technique for optimizing the use of constrained resources, where the relationship between variables is linear.

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Constraint Processing

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Binary C ONSTRAINT P ROCESSING Chapter 2

Binary C ONSTRAINT P ROCESSING Chapter 2. ICS-275A Fall 2007. Class 1 Constraint Networks. The constraint network model Inference. Class description. Instructor: Rina Dechter Days: Monday & Wednesday Time: 11:00 - 12:20 pm 11:00 - 13:00 pm (weeks 1,2)

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Constraint Satisfaction Problems

Constraint Satisfaction Problems. A Brief Summary of Basic Ideas. Outline. What is CSP? Definitions and Terminologies CSP Examples Solving CSP by Search Backtracking Search Heuristics for CSP Search Variable and Value Heuristics Forward Checking and Constraint Propagation Summary.

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Two-Stage Constraint Based Sanskrit Parser

Two-Stage Constraint Based Sanskrit Parser. Akshar Bharati, IIIT,Hyderabad. Brief outline. Dependency Paninian framework vibhakti-karaka correspondence karaka frames (basic + transformation) Source groups, demand groups Constraints Three basic constraints

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Subexponential Algorithms for Unique Games and Related Problems

Subexponential Algorithms for Unique Games and Related Problems. Sanjeev Arora Princeton University & CCI. Boaz Barak MSR New England & Princeton. David Steurer MSR New England. U. School on Approximability , Bangalore, January 2011. Unique Games.

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Complexity of Games & Puzzles [Demaine, Hearn & many others]

Complexity of Games & Puzzles [Demaine, Hearn & many others]. Rengo Kriegspiel?. unbounded. EXPTIME. Undecidable. PSPACE. PSPACE. bridge?. bounded. NP. PSPACE. P. NEXPTIME. team, imperfect info. 0 players (simulation). 1 player (puzzle). 2 players (game).

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Extracting Semantic Constraint from Description Text for Semantic Web Service Discovery

Extracting Semantic Constraint from Description Text for Semantic Web Service Discovery. Dengping Wei, Ting Wang, Ji Wang, and Yaodong Chen Reporter: Ting Wang Department of Computer Science and Technology School of Computer National University of Defense Technology, China

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Robert J. Woodward Constraint Systems Laboratory Department of Computer Science & Engineering

Relational Neighborhood Inverse Consistency for Constraint Satisfaction: A Structure-Based Approach for Adjusting Consistency & Managing Propagation. Robert J. Woodward Constraint Systems Laboratory Department of Computer Science & Engineering University of Nebraska-Lincoln.

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Linear Constraint Graph for Floorplan Optimization with Soft Blocks

Linear Constraint Graph for Floorplan Optimization with Soft Blocks. Jia Wang Electrical and Computer Engineering Illinois Institute of Technology Chicago, IL 60616 Hai Zhou Electrical Engineering and Computer Science Northwestern University Evanston, IL 60208

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ACG - Adjacent Constraint Graph for General Floorplans

ACG - Adjacent Constraint Graph for General Floorplans. Hai Zhou and Jia Wang ICCD 2004, San Jose October 11-13, 2004. Outline. Floorplan and Constraint Graph Adjacent Constraint Graph Data Structure and Operations Experimental Result Summary. Floorplan.

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Enhancing engineering design and analysis interoperability, Part 1. Constrained Objects

Enhancing engineering design and analysis interoperability, Part 1. Constrained Objects. First M.I.T. Conference on Computational Fluid and Solid Mechanics June, 2001 Miyako W. Wilson, Russell Peak, and Robert E. Fulton Georgia Institute of Technology. Motivation.

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Constraint Satisfaction & Knowledge Representation

Constraint Satisfaction & Knowledge Representation. Outline. Constraint Satisfaction Problems (CSP) Knowledge Representation in Prolog Backtracking search for CSPs. Constraint satisfaction problems (CSPs). Standard search problem: state goal test successor function CSP:

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Distributed Scheduling

Distributed Scheduling. What is Distributed Scheduling?. Scheduling: A resource allocation problem Often very complex set of constraints Tied directly to planning: the binding between the plan and the resources Distributed: Each node has only partial information (non-global)

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Optimal Layout Decomposition for Double Patterning Technology

Optimal Layout Decomposition for Double Patterning Technology. Xiaoping Tang, Minsik Cho IBM T.J. Waston Research Center ICCAD 2011. Outline. Introduction Problem formulation Decomposition method Experimental results Conclusion. Introduction.

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Maintaining Arc Consistency

Maintaining Arc Consistency. We have a constraint graph G of variables X 1 ,...X n, and constraint relations {X i ? X j}, and each Xi has a value set V (X i ). An arc from Xi to Xj is consistent if ?v ? V (X i ) ?w ? V (X j ) ? v,w is consistent

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