Feasible Point Pursuit and Successive Approximation of Non-Convex QCQPs

Stochastic Successive Convex Approximation for Non-Convex Constrained Stochastic Optimization

This paper proposes a constrained stochastic successive convex approximation (CSSCA) algorithm to find a stationary point for a general non-convex stochastic optimization problem, whose objective and constraint functions are nonconvex and involve expectations over random states. The existing methods for non-convex stochastic optimization, such as the stochastic (average) gradient and stochastic...

Feasible Region Approximation Using Convex Polytopes

A new technique for polytope approximation of the feasible region for a design is presented. This method is computationally less expensive than the simplicial approximation method [1]. Results on several circuits are presented, and it is shown that the quality of the polytope approximation is substantially better than an ellipsoidal approximation.

Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization

Consider the problem of minimizing the sum of a smooth (possibly non-convex) and a convex (possibly nonsmooth) function involving a large number of variables. A popular approach to solve this problem is the block coordinate descent (BCD) method whereby at each iteration only one variable block is updated while the remaining variables are held fixed. With the recent advances in the developments ...

Approximation of Non-convex Functionals in Gbv Approximation of Non-convex Functionals in Gbv

Pursuit and Evasion in Non-convex Domains of Arbitrary Dimensions

Most results in pursuit-evasion games apply only to planar domains or perhaps to higher-dimensional domains which must be convex. We introduce a very general set of techniques to generalize and extend certain results on simple pursuit to non-convex domains of arbitrary dimension which satisfy a coarse curvature condition (the CAT(0) condition). I. PURSUIT / EVASION There is a significant litera...