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Topic: Discrete optimization

Related Topics

Combinatorial optimization

Knapsack problem

List of combinatorics topics

Polynomial time

Combinatorics

Partition of a set

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	Discrete Optimization Book - Discrete Mathematics and Combinatorics Books
		Discrete Optimization - book by Boros, E.;Hammer, P.L. The book entitled: "Discrete Optimization" is in the Discrete Mathematics and Combinatorics Books section of Mathematics Books area...
		This volume is an update on the impressive progress achieved by mathematicians, operations researchers, and computer scientists in solving discrete optimization problems of very large sizes.
		Discrete location problems with push-pull objectives (J. Krarup, D. Pisinger, F. Plastria).
	www.buysciencebooks.org /discrete-mathematics-and-combinatorics--0-444-51295-0.asp (325 words)

	Discrete Optimization - Elsevier
		Discrete Optimization publishes research papers on the mathematical, computational and applied aspects of all areas of integer programming and combinatorial optimization.
		In addition to reports on mathematical results pertinent to discrete optimization, the journal welcomes submissions on algorithmic developments, computational experiments, and novel applications (in particular, large-scale and real-time applications).
		Manuscripts submitted for possible publication to Discrete Optimization should report on original research, should not have been previously published, and should not be under consideration for publication by any other journal.
	www.elsevier.com /locate/disopt (204 words)

	Discrete Optimization - Elsevier
		This volume is an update on the impressive progress achieved by mathematicians, operations researchers, and computer scientists in solving discrete optimization problems of very large sizes.
		The surveys in this volume present a comprehensive overview of the state of the art in discrete optimization and are written by the most prominent researchers from all over the world.
		This volume describes the tremendous progress in discrete optimization achieved in the last 20 years since the publication of Discrete Optimization '77, Annals of Discrete Mathematics, volumes 4 and 5, 1979 (Elsevier).
	www.elsevier.com /inca/publications/store/6/7/2/6/8/2/index.htt (461 words)

	Discrete Optimization Methods (Site not responding. Last check: )
		The Pascal procedures available in this archive are taken with permission from Discrete Optimization Algorithms with Pascal Programs by Maciej M.
		The field of discrete optimization (as viewed by the authors of the text above) consists of the areas of linear and integer programming, cover problems, knapsack problems, graph theory, network-flow problems, and scheduling.
		Their text covers these areas, using Pascal programs to elucidate methods of attacking discrete optimization problems.
	www.cs.sunysb.edu /~algorith/implement/syslo/implement.shtml (277 words)

	Optimization (Site not responding. Last check: )
		He is especially interested in problems of legislative apportionment, in the connections between apportionment methods and issues in local/global optimization, and in axiomatic approaches and related impossibility theorems.
		Shane Henderson does research on simulation optimization, that is, on optimization problems where the objective function and/or constraint functions are evaluated using simulation.
		He also is intrigued by optimization problems that are naturally viewed from the perspective of functional analysis and its rich duality theory.
	www.orie.cornell.edu /orie/research/fields/optimization/index.cfm (566 words)

	Combinatorial optimization - Wikipedia, the free encyclopedia
		Combinatorial optimization is a branch of optimization in applied mathematics and computer science, related to operations research, algorithm theory and computational complexity theory that sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering.
		Combinatorial optimization algorithms solve instances of problems that are believed to be hard in general, by exploring the usually-large solution space of these instances.
		The domain of combinatorial optimization is optimization problems where the set of feasible solutions is discrete or can be reduced to a discrete one, and the goal is to find the best possible solution.
	en.wikipedia.org /wiki/Combinatorial_optimization (444 words)

	Encyclopedia :: encyclopedia : Optimization (Site not responding. Last check: )
		In mathematics, optimization is the discipline which is concerned with finding the maxima and minima of functions, possibly subject to constraints.
		An example of an optimization problem is the following: maximize the profit of a manufacturing operation while ensuring that none of the resources exceed certain limits and also satisfying as much of the demand faced as possible.
		In computer science, optimization is the process of improving a system in certain ways to increase the effective execution speed and/or bandwidth, or to reduce memory requirements.
	www.hallencyclopedia.com /Optimization (228 words)

	concepts (Site not responding. Last check: )
		Discrete optimization is very different, difficult, diverse, and continues to develop even today.
		The problems addressed by the discrete optimization research community are mainly in the area of operations research usually characterized by linear models.
		The typical approach for incorporating discrete design variables in engineering is to solve the corresponding continuous optimization problem and adjust the optimal design to the nearest discrete values (this is similar to the rounding process to arrive at an integer number).
	www.rit.edu /~pnveme/EMEM820n/Mod7_Discrete/Mod7_content/mod7_sec1_concepts.html (324 words)

	IMA Thematic Year on Optimization, September 2002 - June 2003
		Recently several new optimization paradigms and approaches have been proposed which not only have generated a large body of extremely important algorithmic research but have also given birth to new and widely diverse areas to which mathematical optimization is now being and can be applied.
		In the case of SDP, this has created interest in the optimization community in such areas as robust optimization (optimization when the problem data is subject to certain types of uncertainty), eigenvalue optimization, robust control and stochastic control.
		The general objective is to optimize the parameters expressing the observed data by means of a particular model, and in so doing optimize the microeconomic business objectives of the enterprise.
	www.ima.umn.edu /optimization (2797 words)

	IGK Complex Processes
		Besides applications in optimization problems in engineering, the algorithms may also be applied to model identification problems from cell biology, where measurement data shall be optimally reproduced by differential equation models with unknown structure, depending on the existence or non-existence of reaction pathways from a given set.
		The resulting large discrete optimization problems have to be solved by efficient methods such as SQP-multigrid methods which largely exploit the structure of the problem inherited from the underlying continuous model.
		This adaptive discretization in optimization has been used in [3] for problems in optimal flow control such as drag minimization in pipe flow and the identification of viscosity parameters.
	www.sfb359.uni-heidelberg.de /organization/gk/main.php?name=projects (7457 words)

	The Math Forum - Math Library - Discrete Math
		A centre whose aim is to carry out and promote research and teaching in discrete mathematics and computing, and to facilitate collaborative research with groups working in related areas in Australia and overseas.
		Discrete math problems from a variety of sources, including textbooks, math contests, NCTM books, and puzzle books, and real-life situations, designed to reflect different levels of difficulty.
		A glossary of discrete math terms, from "and" and "absorption law" through "characteristic equation," "negation," and "recurrence relation," to "universal bound law" and "vacuously true." Available for navigation without frames.
	mathforum.org /library/topics/discrete (2153 words)

	Discrete Optimization at Darmstadt University of Technology
		Discrete Optimization has set up as an important component in modern applied mathematics.
		Many problems from business and industry can be modeled as discrete optimization problems.
		This includes the development of mathematical models of real-world problems, the theoretical analysis (using methods mainly from graph theory, polyhedral combinatorics and integer programming), and the design and implementation of fast algorithms as well as their evaluation in practice.
	www.opt.tu-darmstadt.de /forschung/diskopt/index.php (172 words)

	IPCO 2002: About the Summer School
		We propose an approach to address data uncertainty for discrete optimization problems that allows controlling the degree of conservatism of the solution, and is computationally tractable both practically and theoretically.
		In particular, when both the cost coefficients and the data in the constraints of an integer programming problem are subject to uncertainty, we propose a robust integer programming problem of moderately larger size that allows to control the degree of conservatism of the solution in terms of probabilistic bounds on constraint violation.
		When only the cost coefficients are subject to uncertainty and the problem is a 0-1 discrete optimization problem on n variables, then we solve the robust counterpart by solving n+1 instances of the original problem.
	mit.edu /ipco2002/summerschool.html (1590 words)

	Discrete Optimization Using Mathematica -- from Mathematica Information Center
		Many important classes of optimization problems are discrete in nature.
		Ways to apply this technology to various problems in discrete optimization will be discussed.
		Discrete Optimization in Mathematica 4.2 [in Conference Proceedings]
	library.wolfram.com /infocenter/Conferences/4317 (173 words)

	No Title (Site not responding. Last check: )
		Discrete optimization and integer programming provide a paradigm with which a variety of industry and real-world problems can be solved.
		The first application (on scheduling) can be modeled as an integer linear programming problem; the second (on uncapacitated fixed charge network flow) is a mixed-integer problem with an underlying network structure that also has a graph-theoretic model.
		We will describe the aforementioned problems, present the issues involved in each of the models, and show the approaches through which these problems are solved.
	cda.mrs.umn.edu /~pehng/Model/modelp1/modelp1.html (94 words)

	[No title]
		Abstract: The question of nonemptiness of the intersection of a nested sequence of closed sets is fundamental in a number of important optimization topics, including the existence of optimal solutions, the validity of the minimax inequality in zero sum games, and the absence of a duality gap in constrained optimization.
		Abstract: We consider the approximate solution of discrete optimization problems using procedures that are capable of magnifying the effectiveness of any given heuristic algorithm through sequential application.
		We assess performance of resulting solutions relative to optimized s-type ("order-up-to" policies), which are generally accepted as reasonable heuristics for the types of problems we consider.
	web.mit.edu /dimitrib/www/publ.html (15850 words)

	ElsevierMathematics (Site not responding. Last check: )
		Discrete Optimization was launched in the summer of 2004 and will publish 4 issues in 2005, the first of which is now available on ScienceDirect.*
		In addition to reports on foundational and algorithmic theory of discrete optimization, the journal welcomes submissions on experimental and computational discrete optimization, and on novel applications (in particular, large-scale and real-time applications).
		Discrete Optimization is a new addition to the Discrete Mathematics and Discrete Applied Mathematics journals published by Elsevier.
	www.extranet.elsevier.com /listman/computer/discopt.html (274 words)

	Mahmoud Alrefaei's Home Page (Site not responding. Last check: )
		"Discrete stochastic optimization via a modification of the stochastic ruler method." In the Proceedings of the 1996 Winter Simulation Conference.
		Discrete stochastic optimization using variants of the stochastic ruler method.
		An approach for accelerating the convergence of discrete stochastic optimization procedures." INFORMS Fall 1995 National Conference in New Orleans.
	www.just.edu.jo /~alrefaei (841 words)

	MATLAB OPTIMIZATION, LABVIEW and .NET (C++, C#) - Tomlab Optimization
		The TOMLAB Optimization Environment is a powerful optimization platform for solving applied optimization problems in Matlab.
		TOMVIEW enables the solution of a new range of problem types in LabVIEW; everything from process scheduling, discrete and continuous design optimization of simulation/hardware systems and large-scale nonlinear/exponential fitting are handled in a fully integrated platform.
		Well-known optimization solvers, such as SNOPT and MINOS are fully integrated.
	tomopt.com (128 words)

	Discrete Optimization at Darmstadt University of Technology (Site not responding. Last check: )
		Discrete Optimization has set up as an important component in modern applied mathematics.
		Many problems from business and industry can be modeled as discrete optimization problems.
		This includes the development of mathematical models of real-world problems, the theoretical analysis (using methods mainly from graph theory, polyhedral combinatorics and integer programming), and the design and implementation of fast algorithms as well as their evaluation in practice.
	wwwopt.mathematik.tu-darmstadt.de /forschung/diskopt/index.php (172 words)

	Alan Kaminsky -- Comparison of Discrete Optimization Approaches (Site not responding. Last check: )
		A discrete optimization (or combinatorial optimization) problem is to find the minimum or maximum of some function over its entire domain, where the domain is a discrete space.
		A classic example is the traveling salesman problem, where the function is a mapping from a tour of the cities to the length of the tour.
		For this master's project, the student will survey the discrete optimization literature; pick a number of discrete optimization problems; pick a number of solution techniques; write a series of programs to solve each problem using each technique; investigate each program's performance on a range of inputs (i.e.
	www.cs.rit.edu /~ark/research/discreteoptimization.shtml (403 words)

	Summary of Complexity Analysis for Discrete Optimization
		A discrete optimization problem involves the selection of a best alternative from among a finite set of feasible solutions as defined by an objective function.
		As a technical point, usually optimization problems are phrased as decision problems: "is there a solution with value at most B?" If one can efficiently answer the decision problem, then one can answer the optimization question using binary search.
		Examples of useful optimization problems solvable in P are: minimum spanning tree, maximum matching, maximum (network) flow, shortest paths, and linear programming.
	www.cs.sandia.gov /~caphill/complexity.html (1055 words)

	Handbook on Modelling for Discrete Optimization - New books - Publications - Press and Information Office - LSE
		This handbook couples the difficult, critical-thinking aspects of mathematical modelling with the hot area of discrete optimization.
		The book is divided into two parts: one dealing with general methods in the modelling of discrete optimization problems and the other with specific applications.
		The first chapter of this volume, written by H Paul Williams, can be regarded as a basic introduction of how to model discrete optimization problems as mixed integer problems, and outlines the main methods of solving them.
	www.lse.ac.uk /collections/pressAndInformationOffice/publications/books/2006/Handbook_onModelling_forDiscreteOptimization.htm (407 words)

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	www.seoimage.com /search-engine-optimization.html (757 words)

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