24,392 research outputs found
Stochastic Primal-Dual Coordinate Method for Nonlinear Convex Cone Programs
Block coordinate descent (BCD) methods and their variants have been widely
used in coping with large-scale nonconstrained optimization problems in many
fields such as imaging processing, machine learning, compress sensing and so
on. For problem with coupling constraints, Nonlinear convex cone programs
(NCCP) are important problems with many practical applications, but these
problems are hard to solve by using existing block coordinate type methods.
This paper introduces a stochastic primal-dual coordinate (SPDC) method for
solving large-scale NCCP. In this method, we randomly choose a block of
variables based on the uniform distribution. The linearization and Bregman-like
function (core function) to that randomly selected block allow us to get simple
parallel primal-dual decomposition for NCCP. The sequence generated by our
algorithm is proved almost surely converge to an optimal solution of primal
problem. Two types of convergence rate with different probability (almost
surely and expected) are also obtained. The probability complexity bound is
also derived in this paper
Sampling-Based Approaches for Multimarginal Optimal Transport Problems with Coulomb Cost
The multimarginal optimal transport problem with Coulomb cost arises in
quantum physics and is vital in understanding strongly correlated quantum
systems. Its intrinsic curse of dimensionality can be overcome with a
Monge-like ansatz. A nonconvex quadratic programmming then emerges after
employing discretization and penalty. To globally solve this nonconvex
problem, we adopt a grid refinements-based framework, in which a local solver
is heavily invoked and hence significantly determines the overall efficiency.
The block structure of this nonconvex problem suggests taking block coordinate
descent-type methods as the local solvers, while the existing ones can get
seriously afflicted with the poor scalability induced by the associated
sparse-dense matrix multiplications. In this work, borrowing the tools from
optimal transport, we develop novel methods that favor highly scalable schemes
for subproblems and are completely free of the full matrix multiplications
after introducing entrywise sampling. Convergence and asymptotic properties are
built on the theory of random matrices. The numerical results on several
typical physical systems corroborate the effectiveness and better scalability
of our approach, which also allows the first visualization for the approximate
optimal transport maps between electrons in three-dimensional contexts.Comment: 31 pages, 6 figures, 3 table
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