Numerical optimization techniques
Numerical optimization techniques
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Parallel Processing and Parallel Algorithms: Theory and Computation
Parallel Processing and Parallel Algorithms: Theory and Computation
Parallel Computing in Optimization
Parallel Computing in Optimization
Global Optimization with Non-Convex Constraints - Sequential and Parallel Algorithms (Nonconvex Optimization and its Applications Volume 45) (Nonconvex Optimization and Its Applications)
Linear and quadratic programming approaches for the general graph partitioning problem
Journal of Global Optimization
Global search perspectives for multiobjective optimization
Journal of Global Optimization
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More and more optimization problems arising in practice can not be solved by traditional optimization techniques making strong suppositions about the problem (differentiability, convexity, etc.). This happens because very often in real-life problems both the objective function and constraints can be multiextremal, non-differentiable, partially defined, and hard to be evaluated. In this paper, a modern approach for solving such problems (called global optimization problems) is described. This approach combines the following innovative and powerful tools: fractal approach for reduction of the problem dimension, index scheme for treating constraints, non-redundant parallel computations for accelerating the search. Through the paper, rigorous theoretical results are illustrated by figures and numerical examples.