Download Advances in Computational and Stochastic Optimization, Logic by J. N. Hooker (auth.), David L. Woodruff (eds.) PDF

By J. N. Hooker (auth.), David L. Woodruff (eds.)

Computer technological know-how and Operations examine proceed to have a synergistic courting and this booklet - as part of theOperations learn and machine technological know-how Interface Series - sits squarely within the heart of the confluence of those technical examine groups. The examine awarded within the quantity is proof of the increasing frontiers of those intersecting disciplines and gives researchers and practitioners with new paintings within the components of common sense programming, stochastic optimization, heuristic seek and post-solution research for integer courses.
The bankruptcy subject matters span the spectrum of program point. the various chapters are hugely utilized and others symbolize paintings within which the appliance power is simply starting. moreover, each one bankruptcy comprises expository fabric and reports of the literature designed to augment the participation of the reader during this increasing interface.

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Additional resources for Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search: Interfaces in Computer Science and Operations Research

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1952). The problem of simplifying truth functions, American Mathematical Monthly 59,521-531. [25] Quine, W. V. (1952). A way to simplify truth functions, American Mathematical Monthly 62,627-631. [26] Robinson, J. A. (1965). A machine-oriented logic based on the resolution principle, Journal of the ACM 12, 23-41. [27] Saxe, J. (1980). Dynamic programming algorithms for recognizing small bandwidth graphs in polynomial time, SIAM Journal on Algebraic and Discrete M ethods 1, 363-369. [28] Tsang, E.

X6 is found right away. 26 INTERFACES IN COMPUTER SCIENCE AND OPERATIONS RESEARCH Let G be the dependency graph of a constraint set. To be determined is whether there is an ordering of variables for which G has bandwidth ~k. Let A be the set of act i ve nodes (V, E) of the search tree, where initially V = E = 0. Initialize done(V,E) = false for all ordered subsets V of nodes of G and all subsets E of arcs of G. While A is nonempty { Remove a node (V, E) from A, and let V = {Xl,' .. ,Xd}. For each node Xj with (Xl,Xj) E E (or, i f E 0, for each node Xj of G) { = = Child(V,E,xj).

The algorithm actually checks whether there is an ordering of variables for which the dependency graph has bandwidth of at most k. It assumes that the graph is connected, which is innocuous, because components of a disconnected graph can be handled separately. The algorithm builds an ordered list of variables one by one. At a given node of the search tree variables Xl, ... , Xd have been ordered. If Xt is the first of these variables that is adjacentto some unordered node, the algorithm stores only the sublist Xt, ••• , Xd.

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