We establish the existence of minimizers in a rather general setting of dynamic stochastic optimization without assuming either convexity or coercivity of the objective function. We apply this to prove the existence of optimal portfolios for non-concave utility maximization problems in financial market models with frictions (such as illiquidity), a first result of its kind. The proofs are based...

Linear stochastic programming problems with first order stochastic dominance (FSD) constraints are non-convex. For their mixed 0–1 linear programming formulation we present two convex relaxations based on second order stochastic dominance (SSD). We develop necessary and sufficient conditions for FSD, used to obtain a disjunctive programming formulation and to strengthen one of the SSD-based rel...

In recent years, several convex programming relaxations have been proposed to estimate the permanent of a non-negative matrix, notably in the works of [GS02, Gur11, GS14]. However, the origins of these relaxations and their relationships to each other have remained somewhat mysterious. We present a conceptual framework, implicit in the belief propagation literature, to systematically arrive at ...

reliability investigation has always been one of the most important issues in power systems planning. the outages rate in power system reflects the fact that more attentions should be paid on reliability indices to supply consumers with uninterrupted power. using reliability indices in economic dispatch problem may lead to the system load demand with high reliability and low probability of powe...

In this paper, convex semi-infinite programming is converted to an optimal control model of neural networks and the optimal control model is solved by iterative dynamic programming method. In final, numerical examples are provided for illustration of the purposed method.