5 edition of Constraint programming and large scale discrete optimization found in the catalog.
Includes bibliographical references.
|Statement||Eugene C. Freuder, Richard J. Wallace, editors.|
|Series||DIMACS series in discrete mathematics and theoretical computer science -- v. 57.|
|Contributions||Freuder, Eugene C., Wallace, Richard J. 1943-|
|LC Classifications||QA76.612 .D56 1998|
|The Physical Object|
|Pagination||vii, 175 p. :|
|Number of Pages||175|
|LC Control Number||00066354|
Purchase Discrete Optimization, Volume 11 - 1st Edition. Print Book & E-Book. ISBN , when applied to large-scale systems. The resulting algorithm is related to the reduced-gradient method of Wolfe  and the variable-reduction method of McCormick [41, 42]. It also draws much from the unconstrained and linearly- constrained optimization methods of .
for constrained optimization The COCONUT Benchmark - A benchmark for global optimization and constraint satisfaction. A large collection of constrained global optimization testproblems in GAMS format is documented in the book Handbook of Test Problems in Local and Global Optimization By C.A. Floudas et al., Kluwer, Dordrecht () Interior-Point Solver for Large-Scale Quadratic Programming Problems with Bound Constraints. Journal of Optimization Theory and Applications , () An interior point Newton-like method for non-negative least-squares problems with degenerate solution.
The power of modeling in constraint programming. Modeling in constraint programming revolves around the details of what is possible. For example, if you need to schedule a large number of resources and activities that respect capacity limitations, operational sequencing requirements, and business policies while meeting individual customer service goals, these . This book, developed through class instruction at MIT over the last 15 years, provides an accessible, concise, and intuitive presentation of algorithms for solving convex optimization problems. It relies on rigorous mathematical analysis, but also aims at an intuitive exposition that makes use of visualization where possible.
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Get this from a library. Constraint Programming and Large Scale Discrete Optimization. [Eugene C Freuder; Richard J Wallace] -- Constraint programming has become an important general approach for solving hard combinatorial problems that occur in a number of application domains, such as scheduling and configuration.
This. Constraint programming has become an important general approach for solving hard combinatorial problems that occur in a number of application domains, such as scheduling and configuration. This volume contains selected papers from the workshop on Constraint Programming and Large Scale Discrete Optimization held at DIMACS.
ISBN: OCLC Number: Description: vii, pages: illustrations ; 27 cm. Contents: Introduction to DIMACS workshop on constraint programming and large scale discrete optimization / Richard J.
Wallace and Eugene C. Freuder --Using global constraints for local search / Alexander Nareyek --Guided local search joins the elite in discrete. In book: Constraint Programming and Large Scale Discrete Optimization, pp a remarkably rich variety of problems can be represented by discrete optimization models.
This. Constraint Programming (CP) has proven to be a very successful technique for reasoning about assignment problems, as evidenced by the many applications described elsewhere in this book. Much of its success is due to the simple and elegant underlying formulation: describe the world in terms of decision variables that must be assigned values, place clear and explicit restrictions.
And for the large-scale problems with different complex constraints and objective functions, the proposed algorithm obtained new solutions better than mathematical models, GA, and LCA did. Through comparative study, the proposed approach has shown the effectiveness of solving large scale MMSP.
The complexity of MMSP is not limited to large scale. Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization The chapter describes methods for open constraint programming and.
Scope. As opposed to continuous optimization, some or all of the variables used in a discrete mathematical program are restricted to be discrete variables—that is, to assume only a discrete set of values, such as the integers.
Branches. Three notable branches of discrete optimization are: combinatorial optimization, which refers to problems on graphs, matroids and other discrete. Finite domain constraint programming systems. Chap. 14 in [RBW] [Ba] R. Barták. Theory and Practice of Constraint Propagation. Proceedings of CPDC Workshop,[Be] Peter van Beek, Backtracking Search Algorithms, pp in [RBW] [GPP] I.P.
Gent, K.E. Petrie and J.-F. Puget. Symmetry in Constraint Programming, pp in [RBW]. Constraint programming is an optimization technique that emerged from the field of artificial intelligence.
It is characterized by two key ideas: To express the optimization problem at a high level to reveal its structure and to use constraints to reduce the search space by removing, from the variable domains, values that cannot appear in solutions. Appa G., Mourtos I., Magos D.
() Integrating Constraint and Integer Programming for the Orthogonal Latin Squares Problem. In: Van Hentenryck P.
(eds) Principles and Practice of Constraint Programming - CP DIMACS Workshop on Constraint Programming and Large Scale Discrete Optimization SeptemberDIMACS Center, CoRE Building, Busch Campus, Rutgers University, Piscataway, NJ Organizers: Eugene C. Freuder, University of New Hampshire Richard J.
Wallace, University of New Hampshire. This is a substantially expanded (by pages) and improved edition of our best-selling nonlinear programming book.
The treatment focuses on iterative algorithms for constrained and unconstrained optimization, Lagrange multipliers and duality, large scale problems, and on the interface between continuous and discrete optimization. This book provides an up-to-date, comprehensive, and rigorous account of nonlinear programming at the first year graduate student level.
It covers descent algorithms for unconstrained and constrained optimization, Lagrange multiplier theory, interior point and augmented Lagrangian methods for linear and nonlinear programs, duality theory, and major aspects of large-scale optimization.
Mixed‐integer linear fractional program (MILFP) is a class of mixed‐integer nonlinear programs (MINLP) where the objective function is the ratio of two linear functions and all constraints are linear. Global optimization of large‐scale MILFPs can be computationally intractable due to the presence of discrete variables and the pseudoconvex.
The problem is that I need to solve all of these constraints at the same time. So solving any individual one of them is easy, but when I put them all together as a conjunction, it becomes hard. So constraint programming, is an approach to solving discrete optimization problems, and it's made up of two components: propagation and search.
This paper describes the development of a constraint programming (CP) model for schedule optimization to satisfy both deadline and resource constraints in large-scale construction projects. Unlike many meta-heuristic methods in the literature, the CP model is fast and provides near-optimum solutions to projects with hundreds of activities.
The IBM ILOG modeling. This paper describes a practical algorithm for large-scale mean-variance portfolio optimization.
The emphasis is on developing an efficient computational approach applicable to the broad range of portfolio models employed by the investment community. large-scale optimization, nonlinear programming, nonlinear inequality constraints, sequential quadratic programming, quasi-Newton methods, limited-memory methods AMS Subject Headings 49J20, 49J15, 49M37, 49D37, 65F05, 65K05, 90C Network optimization lies in the middle of the great divide that separates the two major types of optimization problems, continuous and discrete.
The ties between linear programming and combinatorial optimization can be traced to the representation of the constraint polyhedron as the convex hull of its extreme points.
Optimization of Large-Scale Hydrothermal System Operation package and can invoke different linear as well as nonlinear programming solvers. The optimization model was applied to the Brazilian hydrothermal system, one of the largest in the world.
Chance-Constrained Optimal Hedging Rules for Cascaded Hydropower Reservoirs.In this paper, we present a new hybrid algorithm for convex Mixed Integer Nonlinear Programming (MINLP). The proposed hybrid algorithm is an improved version of the classical nonlinear branch-and-bound (BB) procedure, where the enhancements are obtained with the application of the outer approximation algorithm on some nodes of the enumeration tree.
The .constraint including geotags (camera locations) and vanish-ing points. The second step of our initialization process is a Levenberg-Marquardt nonlinear optimization, related to bundle adjustment, but involving additional constraints. This hybrid discrete-continuous optimization allows for an efﬁcient search of a very large parameter space of.