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Oral
Blended Conditonal Gradients
Gábor Braun · Sebastian Pokutta · Dan Tu · Stephen Wright

Tue Jun 11 02:20 PM -- 02:25 PM (PDT) @ Room 103
in Convex Optimization »

We present a blended conditional gradient approach for minimizing a smooth convex function over a polytope P, combining the Frank–Wolfe algorithm (also called conditional gradient) with gradient-based steps, different from away steps and pairwise steps, but still achieving linear convergence for strongly convex functions, along with good practical performance. Our approach retains all favorable properties of conditional gradient algorithms, notably avoidance of projections onto P and maintenance of iterates as sparse convex combinations of a limited number of extreme points of P. The algorithm is lazy, making use of inexpensive inexact solutions of the linear programming subproblem that characterizes the conditional gradient approach. It decreases measures of optimality (primal and dual gaps) rapidly, both in the number of iterations and in wall-clock time, outperforming even the lazy conditional gradient algorithms of Braun et al. 2017. We also present a streamlined version of the algorithm that applies when P is the probability simplex.

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Author Information

Gábor Braun (Georgia Institute of Technology)
Sebastian Pokutta (Georgia Tech)
Dan Tu (GEORGIA TECH)
Stephen Wright (University of Wisconsin-Madison)

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