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Convex mixed-integer optimization with Frank–Wolfe methods
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DOI：10.1007/s12532-025-00288-w.png)
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Abstract
En 中文
Mixed-integer nonlinear optimization encompasses a broad class of problems that present both theoretical and computational challenges. We propose a new type of method to solve these problems based on a branch-and-bound algorithm with convex node relaxations. These relaxations are solved with a Frank–Wolfe algorithm over the convex hull of mixed-integer feasible points instead of the continuous relaxation via calls to a mixed-integer linear solver as the linear minimization oracle. The proposed method computes feasible solutions while working on a single representation of the polyhedral constraints, leveraging the full extent of mixed-integer linear solvers without an outer approximation scheme and can exploit inexact solutions of node subproblems.
Keywords:
Nonlinear optimization
Mixed-integer optimization
Branch-and-bound
Frank–Wolfe
Journal
IF:
3.6
Papers:
197
Citations:
1.9K