Dual Interior-Point Optimization Learning
Michael Klamkin Mathieu Tanneau Pascal Van Hentenryck
This paper introduces Dual Interior Point Learning (DIPL) and Dual
Supergradient Learning (DSL) to learn dual feasible solutions to parametric
linear programs with bounded variables, which are pervasive across many
industries. DIPL mimics a novel dual interior point algorithm while DSL mimics
classical dual supergradient ascent. DIPL and DSL ensure dual feasibility by
predicting dual variables associated with the constraints then exploiting the
flexibility of the duals of the bound constraints. DIPL and DSL complement
existing primal learning methods by providing a certificate of quality. They
are shown to produce high-fidelity dual-feasible solutions to large-scale
optimal power flow problems providing valid dual bounds under 0.5% optimality
gap.