Probability Maximization via Minkowski Functionals: Convex Representations and Tractable Resolution

Abstract: In this paper, we consider the maximization of a probability $\mathbb{P}{ \zeta \mid \zeta \in \mathbf{K}(\mathbf x)}$ over a closed and convex set $\mathcal X$, a special case of the chance-constrained optimization problem. We define $\mathbf{K}(\mathbf x)$ as $\mathbf{K}(\mathbf x) \triangleq { \zeta \in \mathcal{K} \mid c(\mathbf{x},\zeta) \geq 0 }$ where $\zeta$ is uniformly distributed on a convex and compact set $\mathcal{K}$ and $c(\mathbf{x},\zeta)$ is defined as either {$c(\mathbf{x},\zeta) \triangleq 1-|\zeta^T\mathbf{x}|^m$, $m\geq 0$} (Setting A) or $c(\mathbf{x},\zeta) \triangleq T\mathbf{x} -\zeta$ (Setting B). We show that in either setting, $\mathbb{P}{ \zeta \mid \zeta \in \mathbf{K(x)}}$ can be expressed as the expectation of a suitably defined function $F(\mathbf{x},\xi)$ with respect to an appropriately defined Gaussian density (or its variant), i.e. $\mathbb{E}_{\tilde p} [F(\mathbf x,\xi)]$. We then develop a convex representation of the original problem requiring the minimization of ${g(\mathbb{E}[F(\mathbf{x},\xi)])}$ over $\mathcal X$ where $g$ is an appropriately defined smooth convex function. Traditional stochastic approximation schemes cannot contend with the minimization of ${g(\mathbb{E}[F(\cdot,\xi)])}$ over $\mathcal X$, since conditionally unbiased sampled gradients are unavailable. We then develop a regularized variance-reduced stochastic approximation (r-VRSA) scheme that obviates the need for such unbiasedness by combining iterative regularization with variance-reduction. Notably, (r-VRSA) is characterized by both almost-sure convergence guarantees, a convergence rate of $\mathcal{O}(1/k^{1/2-a})$ in expected sub-optimality where $a > 0$, and a sample complexity of $\mathcal{O}(1/\epsilon^{6+\delta})$ where $\delta > 0$.