One-dimensional Global Optimization Problems with Multiextremal Constraints
Abstract
Lipschitz univariate constrained global optimization
problems where both the objective function and constraints can be
multiextremal and non-differentiable are considered. The
constrained problem is reduced to a discontinuous unconstrained
problem by the index scheme without introducing additional
parameters or variables. A new geometric method using adaptive estimates of Lipschitz constants is described, its convergence conditions are established.
Numerical experiments including comparison of the new algorithm
with methods using penalty approach are given. Algorithms with local tuning technique on behaviour of both the objective
function and constraints are considered.
[DOI: 10.1685 / CSC06100] About DOI
problems where both the objective function and constraints can be
multiextremal and non-differentiable are considered. The
constrained problem is reduced to a discontinuous unconstrained
problem by the index scheme without introducing additional
parameters or variables. A new geometric method using adaptive estimates of Lipschitz constants is described, its convergence conditions are established.
Numerical experiments including comparison of the new algorithm
with methods using penalty approach are given. Algorithms with local tuning technique on behaviour of both the objective
function and constraints are considered.
[DOI: 10.1685 / CSC06100] About DOI
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PDFDOI: https://doi.org/10.1685/
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