The top documents tagged [shortest tour]

1 The TSP : Approximation and Hardness of Approximation All exact science is dominated by the idea of approximation. -- Bertrand Russell (1872 - 1970)
Documents 1 The TSP : Approximation and Hardness of Approximation All exact science is dominated by the idea of approximation. -- Bertrand Russell (1872 - 1970)

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More NP-completeness Sipser 7.5 (pages 283-294). CS 311 Fall 2008 2 NP’s hardest problems Definition 7.34: A language B is NP-complete if 1.B ∈ NP 2.A≤
Documents More NP-completeness Sipser 7.5 (pages 283-294). CS 311 Fall 2008 2 NP’s hardest problems Definition 7.34: A language B is NP-complete if 1.B ∈ NP 2.A≤

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The Power of Preprocessing Bart M. P. Jansen September 29th, Algorithms & Visualization seminar, TU Eindhoven
Documents The Power of Preprocessing Bart M. P. Jansen September 29th, Algorithms & Visualization seminar, TU Eindhoven

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Modeling Rich Vehicle Routing Problems TIEJ601 Postgraduate Seminar Tuukka Puranen October 19 th 2009
Documents Modeling Rich Vehicle Routing Problems TIEJ601 Postgraduate Seminar Tuukka Puranen October 19 th 2009

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TSP.1 9.4 Travelling Salesperson Problem (TSP) Very famous problem Many practical applications Very easy to describe Very difficult to solve (Curse of
Documents TSP.1 9.4 Travelling Salesperson Problem (TSP) Very famous problem Many practical applications Very easy to describe Very difficult to solve (Curse of

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More NP-completeness Sipser 7.5 (pages 283-294). CS 311 Mount Holyoke College 2 NP’s hardest problems Definition 7.34: A language B is NP-complete if
Documents More NP-completeness Sipser 7.5 (pages 283-294). CS 311 Mount Holyoke College 2 NP’s hardest problems Definition 7.34: A language B is NP-complete if

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Local search algorithms In many optimization problems, the state space is the space of all possible complete solutions We have an objective function that
Documents Local search algorithms In many optimization problems, the state space is the space of all possible complete solutions We have an objective function that

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Two types of search problems Start state is given Goal state is known ahead of time Solution path matters No specific start state Goal state is unknown
Documents Two types of search problems Start state is given Goal state is known ahead of time Solution path matters No specific start state Goal state is unknown

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Neural Networks for Optimization William J. Wolfe California State University Channel Islands
Documents Neural Networks for Optimization William J. Wolfe California State University Channel Islands

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Neural Networks for Optimization Bill Wolfe California State University Channel Islands
Documents Neural Networks for Optimization Bill Wolfe California State University Channel Islands

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Promoting Student Engagement with Classroom Presenter Richard Anderson University of Washington 3/6/2007Carnegie Mellon University1
Documents Promoting Student Engagement with Classroom Presenter Richard Anderson University of Washington 3/6/2007Carnegie Mellon University1

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CSE 101, Winter 2002 Design and Analysis of Algorithms Instructor: Andrew B. Kahng, Email: abk@ucsd.edu Telephone: 858-822-4884
Documents CSE 101, Winter 2002 Design and Analysis of Algorithms Instructor: Andrew B. Kahng, Email: [email protected] Telephone: 858-822-4884

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