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A multidimensional descent method for global optimization
Optimization, 2009 This article presents a new multidimensional descent method for solving global optimization problems with box-constraints. This is a hybrid method where local search method is used for a local descent and global search is used for further ...
Adil Bagirov, Jiapu Zhang
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A new auxiliary function method for general constrained global optimization
Optimization, 2013 In this article, we first propose a method to obtain an approximate feasible point for general constrained global optimization problems (with both inequality and equality constraints).
Musa Mammadov
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Parametric Method for Global Optimization
Journal of Optimization Theory and Applications, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
De Marchi, S., Raykov, I.
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Method of constrained global optimization
Physical Review Letters, 1994We present a new method for optimization: constrained global optimization (CGO). CGO iteratively uses a Glauber spin flip probability and the Metropolis algorithm. The spin flip probability allows changing only the values of variables contributing excessively to the function to be minimized.
, Altschuler +4 more
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A Stochastic Method for Constrained Global Optimization
SIAM Journal on Optimization, 1994The authors consider a global minimum problem in \(\mathbb{R}^ n\) with finite numbers of both equality and inequality constraints \(h_ i(x)\leq 0\) and \(h_ i(x)= 0\), where the objective function \(F\) and all \(h_ i\) are \(C^ 2\). The problem is solved by considering the unconstrained problem, but with a penalty term that is multiplied by a penalty
Klaus Ritter, Stefan Schäffler
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Bayesian methods in global optimization
Journal of Global Optimization, 1991The paper reviews methods which have been proposed for solving global optimization problems in the framework of the Bayesian paradigm. Three main approaches are singled out. In the first approach, called the Random Function Approach, methods are based on the idea of introducing a probabilistic model for the objective function in the form of a random ...
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2002
Training a neural network is a difficult optimization problem because of numerous local minimums. Many global search algorithms have been used to train neural networks. However, local search algorithms are more efficient with computational resources, and therefore numerous random restarts with a local algorithm may be more effective than a global ...
Hamm, Lonnie +3 more
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Training a neural network is a difficult optimization problem because of numerous local minimums. Many global search algorithms have been used to train neural networks. However, local search algorithms are more efficient with computational resources, and therefore numerous random restarts with a local algorithm may be more effective than a global ...
Hamm, Lonnie +3 more
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A Smoothing Method of Global Optimization that Preserves Global Minima
Journal of Global Optimization, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mark S. K. Lau, C. P. Kwong
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CLUSTERING METHODS IN GLOBAL OPTIMIZATION
IFAC Proceedings Volumes, 1986Abstract Global optimization methods are designed to be used for problems where the objective function may have several local minima (maxima). Such problems are in general unsolvable and therefore the methods are designed to maximize the probability of discovering the global minimum. One class of such methods are the so called clustering methods.
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The Method of Moments in Global Optimization
Journal of Mathematical Sciences, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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