Deterministic and Stochastic Frank–Wolfe Recursion on Probability Spaces

Motivated by applications in emergency response and experimental design, we consider smooth stochastic optimization problems over probability measures supported on compact subsets of the Euclidean space. With the influence function as the variational object, we construct a deterministic Frank–Wolfe (dFW) recursion for probability spaces. The dFW recursion is made especially possible by a lemma that identifies the solution to the infinite-dimensional Frank–Wolfe subproblem as a Dirac measure concentrating on the minimum of the influence function at the incumbent iterate. Each iterate in the dFW recursion is thus expressed through a “particle update,” as a convex combination of the incumbent iterate and a Dirac measure. To address common application contexts that have access only to Monte Carlo observations of the objective and influence function, we construct a stochastic Frank–Wolfe (sFW) variation that generates a random sequence of probability measures constructed using minima of increasingly accurate estimates of the influence function. We demonstrate that the sFW optimality gap sequence exhibits $O(k^{-1})$ iteration complexity almost surely and in expectation for smooth convex objectives, and $O(k^{-1/2})$ (in the Frank–Wolfe gap) for smooth nonconvex objectives. Furthermore, we show that an easy-to-implement fixed-step, fixed-sample version of the sFW method exhibits exponential convergence to $\epsilon$-optimality. We end with a central limit theorem on the observed objective values at the sequence of generated random measures. To further intuition, we include several illustrative examples with exact influence function calculations.

Funding: This work was partially supported by the National Science Foundation [Grants CMMI-2035086, DMS-2230023, and OAC-2410950] and by the Office of Naval Research [Grants 13000991 and N000141712295].