Absolute minimizer in convex programming by exponential penalty.
Absolute minimizer in convex programming by exponential penalty.
We consider a nonlinear convex program. Under some general hy-potheses, we prove that approximate solutions obtained by exponential penalty converge toward a particular solution of the original convex program as the penalty parameter goes to zero. This particular solu-tion is called the absolute minimizer and is characterized as the unique solution of a hierarchical scheme of minimax problems.
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Convex programming, penalty methods, convergence, convexity, optimal trajectory, Sensitivity, stability, parametric optimization, Other numerical methods in calculus of variations, nonuniqueness, minimax problems, Minimax problems in mathematical programming
Convex programming, penalty methods, convergence, convexity, optimal trajectory, Sensitivity, stability, parametric optimization, Other numerical methods in calculus of variations, nonuniqueness, minimax problems, Minimax problems in mathematical programming
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