Competition glider flying, like other outdoor sports, is a game of stochastic optimization, in which mathematics and quantitative strategies have historically played an important role. We address the problem of uncertain future atmospheric conditions by constructing a nonlinear Hamilton-Jacobi-Bellman equation for the optimal speed to fly, with a free boundary describing the climb/cruise decisi...

This paper deals with the optimal control problem for systems with state linear equality constraints. For deterministic linear systems, first we find various existence conditions for constraining state feedback control and determine all constraining feedback gains, from which the optimal feedback gain is derived by using the result of singular optimal control. For systems with stochastic proces...

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...

Stochastic Constraint Programming is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. A solution to such a problem is a policy tree that specifies decision variable assignments in each scenario. Several complete solution methods have been proposed, but the authors recently showed that an incomplete approach based on neuroevolution is...

In this paper we address the general multi-period production/inventory problem with non-stationary stochastic demand and supplier lead time under service-level constraints. A replenishment cycle policy (Rn,Sn) is modeled, where Rn is the n-th replenishment cycle length and Sn is the respective order-up-to-level. Initially we extend an existing formulation for this policy in a way to incorporate...

Planning by incomplete stochastic search ooers a promising alternative to classical, complete planning methods. The success of this approach is documented by recent performance results obtained by transforming planning tasks into propositional sat-issability problems and using existing eecient local search methods to solve them. On the other hand, we argue that these results can still be improv...

Modeling in computational systems biology is dominated by two formalisms, the first one being (mainly ordinary) differential equations and the second one being continuous-time stochastic processes [1, 2]. Both methodologies have their roots reaching back to physical and chemical arguments that, at least for modeling biochemical reactions, give strong foundations to the approach. Recently, stoch...

E-GENET shows certain success on extending GENET for non-binary CSP's. However, the generic constraint representation scheme of E-GENET induces the problem of storing too many penalty values in constraint nodes and the min-connicts heuristic is not eecient enough on some problems. To overcome these two weaknesses and further improve the performance, we propose several modiications. All of them ...

Stochastic Constraint Programming is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. A solution to such a problem is a policy tree that specifies decision variable assignments in each scenario. Several solution methods have been proposed but none seems practical for large multi-stage problems. We propose an incomplete approach: spec...

In many real-life optimization problems involving multiple agents, the rewards are not necessarily known exactly in advance, but rather depend on sources of exogenous uncertainty. For instance, delivery companies might have to coordinate to choose who should serve which foreseen customer, under uncertainty in the locations of the customers. The framework of Distributed Constraint Optimization u...