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	Browse All Reviews > Mathematics Of Computing (G) > Numerical Analysis (G.1) > Optimization (G.1.6) > Global Optimization (G.1.6...)     Options: All Media Types Journals Proceedings Div Books Whole Books Other Date Reviewed Title Author Publisher Published Date Descending Ascending     1-10 of 14 Reviews about "Global Optimization (G.1.6...)": Date Reviewed   A supervised particle swarm algorithm for real-parameter optimization Cheung N., Ding X., Shen H. Applied Intelligence 43(4): 825-839, 2015.  Type: Article Optimization using swarm algorithms is a topic that has attracted research since the introduction of the particle swarm optimization (PSO) paradigm in the 1990s. Many variants of the method aim at improving convergence while avoiding l... Dec 30 2015   Global optimization: theory, algorithms, and applications Locatelli M., Schoen F., SIAM, Philadelphia, PA, 2013. 445 pp.  Type: Book (978-1-611972-66-5) The field of global optimization (GO) considers the optimization of an objective function f in a subset S of the real numbers called a feasible region. Since the conversion of maximization problems... Apr 21 2014    Approximate branch-and-bound global optimization using B-spline hypervolumes Park S. Advances in Engineering Software 45(1): 11-20, 2012.  Type: Article Multidimensional unconstrained global optimization arises in a vast spectrum of applications, ranging from financial and physical to biological. Local methods cannot adequately deal with the often numerous local minima and other challe... May 10 2012   Multiobjective search algorithm with subdivision technique Jahn J. Computational Optimization and Applications 35(2): 161-175, 2006.  Type: Article Constrained optimization refers to minimizing (or maximizing) an objective function under constraints based on possible values of the independent variable. In a practical constrained optimization problem, there can be multiple independ... May 11 2007   Differential evolution: a practical approach to global optimization (Natural Computing Series) Price K., Storn R., Lampinen J., Springer-Verlag New York, Inc., Secaucus, NJ, 2005. 538 pp.  Type: Book (9783540209508) The solution of difficult optimization problems using robust, effective, fast, and, above all, easy-to-use algorithms is a very important problem in all fields of science and engineering. The authors present differential evolution (DE)... Jan 23 2007   Accelerating branch-and-bound through a modeling language construct for relaxation-specific constraints Sahinidis N., Tawarmalani M. Journal of Global Optimization 32(2): 259-280, 2005.  Type: Article This paper reports on computational experiences using the branch and reduce optimization navigator (BARON) modeling language for solving global optimization problems. More specifically, the authors extend BARON, introducing the new con... Apr 18 2006   On the multilevel structure of global optimization problems Locatelli M. Computational Optimization and Applications 30(1): 5-22, 2005.  Type: Article Locatelli presents a multilevel view of global optimization (GO) problems. He shows that a GO problem can often be seen at different levels, displaying a similar structure, even if different objects are observed at each level.... Jul 28 2005   Efficient algorithms for large scale global optimization: Lennard-Jones clusters Locatelli M., Schoen F. Computational Optimization and Applications 26(2): 173-190, 2003.  Type: Article A new and very efficient method for the large-scale global optimization of Lennard-Jones (L-J) clusters is presented in this paper. The problem, which consists of minimizing the potential energy of a cluster of particles that interact ... Apr 6 2004   Convergence of alternating optimization Bezdek J., Hathaway R. Neural, Parallel & Scientific Computations 11(4): 351-368, 2003.  Type: Article A local and global convergence theory for the alternating method is presented in this paper. This method is an iterative procedure for minimizing a nonlinear function; at each step, a subset of the variables is selected, and the functi... Mar 29 2004   A greedy EM algorithm for Gaussian mixture learning Vlassis N., Likas A. Neural Processing Letters 15(1): 77-87, 2002.  Type: Article The improvement described in applying Gaussian mixtures (k weighted sums with weights summing to one) to heterogeneous population data employs the expectation-maximization (EM) algorithm in a greedy manner, solving t... Jun 17 2003       Display 510152550100 per page
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