Sequential-coupling convex relaxations using local marginals and cluster moments solve high-dimensional Markov process optimization, recovering Benamou–Brenier dynamics and general kernels as special cases.
Convergent SDP-Relaxations in Polynomial Optimization with Sparsity
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Convex Relaxations for the Optimization of Markov Processes
Sequential-coupling convex relaxations using local marginals and cluster moments solve high-dimensional Markov process optimization, recovering Benamou–Brenier dynamics and general kernels as special cases.