On Smoothing Exact Penalty Functions for Convex Constrained Optimization
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Summary: A quadratic smoothing approximation to nondifferentiable exact penalty functions for convex constrained optimization is proposed and its properties are established. The smoothing approximation is used as the basis of an algorithm for solving problems with (i) embedded network structures, and (ii) nonlinear minimax problems. Extensive numerical results with large-scale problems illustrate the efficiency of this approach.
Large-scale problems in mathematical programming, Convex programming, quadratic smoothing approximation, Other numerical methods in calculus of variations, convex constrained optimization, Deterministic network models in operations research, embedded network structures, nondifferentiable exact penalty functions, nonlinear minimax
Large-scale problems in mathematical programming, Convex programming, quadratic smoothing approximation, Other numerical methods in calculus of variations, convex constrained optimization, Deterministic network models in operations research, embedded network structures, nondifferentiable exact penalty functions, nonlinear minimax
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