Learning Constrained Optimization with Deep Augmented Lagrangian Methods
arxiv(2024)
摘要
Learning to Optimize (LtO) is a problem setting in which a machine learning
(ML) model is trained to emulate a constrained optimization solver. Learning to
produce optimal and feasible solutions subject to complex constraints is a
difficult task, but is often made possible by restricting the input space to a
limited distribution of related problems. Most LtO methods focus on directly
learning solutions to the primal problem, and applying correction schemes or
loss function penalties to encourage feasibility. This paper proposes an
alternative approach, in which the ML model is trained instead to predict dual
solution estimates directly, from which primal estimates are constructed to
form dual-feasible solution pairs. This enables an end-to-end training scheme
is which the dual objective is maximized as a loss function, and solution
estimates iterate toward primal feasibility, emulating a Dual Ascent method.
First it is shown that the poor convergence properties of classical Dual Ascent
are reflected in poor convergence of the proposed training scheme. Then, by
incorporating techniques from practical Augmented Lagrangian methods, we show
how the training scheme can be improved to learn highly accurate constrained
optimization solvers, for both convex and nonconvex problems.
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