Relaxation Methods for Strictly Convex Regularizations of Piecewise Linear Programs

Relaxation Methods for Strictly Convex Regularizations of Piecewise Linear Programs We give an algorithm for minimizing the sum of a strictly convex function and a convex piecewise linear function. It extends several dual coordinate ascent methods for large-scale linearly constrained problems that occur in entropy maximization, quadratic programming, and network flows. In particular, it may solve exact penalty versions of such (possibly inconsistent) problems, and subproblems of bundle methods for nondifferentiable optimization. It is simple, can exploit sparsity, and in certain cases is highly parallelizable. Its global convergence is established in the recent framework of B -functions (generalized Bregman functions). Applied Mathematics and Optimization Springer Journals

Relaxation Methods for Strictly Convex Regularizations of Piecewise Linear Programs

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Copyright © Inc. by 1998 Springer-Verlag New York
Mathematics; Calculus of Variations and Optimal Control; Optimization; Systems Theory, Control; Theoretical, Mathematical and Computational Physics; Mathematical Methods in Physics; Numerical and Computational Physics, Simulation
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