Titlebook: Global Optimization; From Theory to Imple Leo Liberti,Nelson Maculan Book 2006 Springer-Verlag US 2006 Monte Carlo method.Transformation.al

DOSE

https://doi.org/10.1007/978-1-4842-7490-3In this paper we propose two variants of Local Search Method for reverse convex problems. The first is based on well-known theorem of H. Tuy as well as on Linearization Principle. The second variant is due to an idea of J. Rosen. We demonstrate the practical effectiveness of the proposed methods computationally.

吃掉

广大

弯弯曲曲

Disciplined Convex ProgrammingA new methodology for constructing convex optimization models called . is introduced. The methodology enforces a set of conventions upon the models constructed, in turn allowing much of the work required to analyze and solve the models to be automated.

疏远天际

Optimization under Composite Monotonic Constraints and Constrained Optimization over the Efficient Sion is meant a function which is the composition of a monotonic function on ℝ. with a mapping from ℝ. → ℝ. with . ≤ ..) This class includes problems with constraints involving products of linear functions, sums of ratio functions, etc., and also problems of constrained optimization over efficient/we

LVAD360

Some Transformation Techniques in Global Optimization of nonconvex MINLP (mixed integer non-linear programming) problems can be solved to global optimality. The transformations can be applied to signomial functions and the feasible region of the original problem can be convexified and overestimated by the transformations. The global optimal solution o

欢乐中国

Solving Nonlinear Mixed Integer Stochastic Problems: a Global Perspectivetochastic problem with nonlinearities both in the objective function and constraints, pure integer first stage and mixed-integer second stage variables. We formulate the problem by a scenario based representation, adding linear nonanticipativity constraints coming from splitting the first stage deci

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Application of Quasi Monte Carlo Methods in Global Optimization sequences. A quasi Monte Carlo (QMC) variant of a multi level single linkage (MLSL) algorithm for global optimization is compared with an original stochastic MLSL algorithm for a number of test problems of various complexities. An emphasis is made on high dimensional problems. Two different low-dis

fledged

GLOB — A new VNS-based Software for Global Optimizational VNS algorithm is implemented within the software package GLOB. The tests are performed on some standard test functions and on a class of NP-hard global optimization problems arising in practice. The computational results show the potential of the new software.

不朽中国

Writing Global Optimization Softwareiculties involved in coding a good GO software are: embedding third-party local optimization codes within the main global optimization algorithm; providing efficient memory representations of the optimization problem; making sure that every part of the code is fully reentrant. Finding good software