The algebraic eigenvalue problem
The algebraic eigenvalue problem
Iterative solution of nonlinear equations in several variables
Iterative solution of nonlinear equations in several variables
Heuristic Optimizaton and Dynamical System Safety Verification
Hybrid Systems V
A cut-peak function method for global optimization
Journal of Computational and Applied Mathematics
A comparative study of metamodeling methods for multiobjective crashworthiness optimization
Computers and Structures
Performance evaluation and population reduction for a self adaptive hybrid genetic algorithm (SAHGA)
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Simulation optimization based on Taylor Kriging and evolutionary algorithm
Applied Soft Computing
Global descent methods for unconstrained global optimization
Journal of Global Optimization
Hierarchical Knowledge Gradient for Sequential Sampling
The Journal of Machine Learning Research
Extending the GA-EDA hybrid algorithm to study diversification and intensification in GAs and EDAs
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Relative error stochastic kriging
Proceedings of the Winter Simulation Conference
Optimal learning for sequential sampling with non-parametric beliefs
Journal of Global Optimization
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A new method has been developed for solving a system of nonlinear equations g(x) = 0. This method is based on solving the related system of differential equations dg/dt±g(x)= 0 where in the sign is changed whenever the corresponding trajectory x(t) encounters a change in sign of the Jacobian determinant or arrives ata solution point of g(x)= 0. This procedure endows the method with much wider region of convergence than other methods (occasionally, even global convergence) and enableist to find multiple solutions of g(x)= 0 one after the other. The principal limitations of the method relate to the extraneouss ingularities of the differential equation. The role of these singularities is illustrated by several examples. In addition, the extension of the method to the problem of finding multiple extrema of a function of N variables is explained and some examples are given.