DCA-BPCG-WS-ES, a Frank-Wolfe variant with warm-starting and adaptive early stopping, solves constrained DC problems with orders of magnitude fewer linear oracle calls than prior FW-based DCA variants.
Generalized Polyhedral DC Optimization Problems
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abstract
The problem of minimizing the difference of two lower semicontinuous, proper, convex functions (a DC function) on a nonempty closed convex set in a locally convex Hausdorff topological vector space is studied in this paper. The focus is made on the situations where either the second component of the objective function is a generalized polyhedral convex function or the first component of the objective function is a generalized polyhedral convex function and the constraint set is generalized polyhedral convex. Various results on optimality conditions, the local solution set, the global solution set, and solution algorithms via duality are obtained. Useful illustrative examples are considered.
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Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms
DCA-BPCG-WS-ES, a Frank-Wolfe variant with warm-starting and adaptive early stopping, solves constrained DC problems with orders of magnitude fewer linear oracle calls than prior FW-based DCA variants.