Coordinate Linear Variance Reduction for Generalized Linear Programming. (arXiv:2111.01842v1 [math.OC])

We study a class of generalized linear programs (GLP) in a large-scale
setting, which includes possibly simple nonsmooth convex regularizer and simple
convex set constraints. By reformulating GLP as an equivalent convex-concave
min-max problem, we show that the linear structure in the problem can be used
to design an efficient, scalable first-order algorithm, to which we give the
name emph{Coordinate Linear Variance Reduction} (textsc{clvr}; pronounced
“clever”). textsc{clvr} is an incremental coordinate method with implicit
variance reduction that outputs an emph{affine combination} of the dual
variable iterates. textsc{clvr} yields improved complexity results for (GLP)
that depend on the max row norm of the linear constraint matrix in (GLP) rather
than the spectral norm. When the regularization terms and constraints are
separable, textsc{clvr} admits an efficient lazy update strategy that makes
its complexity bounds scale with the number of nonzero elements of the linear
constraint matrix in (GLP) rather than the matrix dimensions. We show that
Distributionally Robust Optimization (DRO) problems with ambiguity sets based
on both $f$-divergence and Wasserstein metrics can be reformulated as (GLPs) by
introducing sparsely connected auxiliary variables. We complement our
theoretical guarantees with numerical experiments that verify our algorithm’s
practical effectiveness, both in terms of wall-clock time and number of data



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