Establishes matching Ω(T^{-p/(p-1)}) lower bounds for Frank-Wolfe on p-uniformly convex feasible sets for p ≥ 3, plus extension to Hölderian error bounds.
In: 22nd International Symposium on Experimental Algorithms
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A column generation method with interior-point SDP solvers solves the continuous relaxation of exact D-optimal experimental design to identify support and construct near-optimal exact designs for large-scale instances.
A mixed-integer convex formulation for graph isomorphism is solved via first-order methods and variable fixing, outperforming integer feasibility on 6 of 12 graph families and matching on symmetric ones.
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Curvature-Dependent Lower Bounds for Frank-Wolfe
Establishes matching Ω(T^{-p/(p-1)}) lower bounds for Frank-Wolfe on p-uniformly convex feasible sets for p ≥ 3, plus extension to Hölderian error bounds.
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A column generation approach to exact experimental design
A column generation method with interior-point SDP solvers solves the continuous relaxation of exact D-optimal experimental design to identify support and construct near-optimal exact designs for large-scale instances.
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Graph Isomorphism: Mixed-Integer Convex Optimization from First-Order Methods
A mixed-integer convex formulation for graph isomorphism is solved via first-order methods and variable fixing, outperforming integer feasibility on 6 of 12 graph families and matching on symmetric ones.