In this paper, we consider the linear programming (LP) formulation for deep reinforcement learning. The number of constraints depends on size state and action spaces, which makes problem intractable in large or continuous environments. general augmented Lagrangian method suffers double-sampling obstacle solving program. Motivated from updates multipliers, overcome obstacles minimizing function ...

For design optimization tasks, quite often a so-called one-shot approach is used. It augments the solution of the state equation with a suitable adjoint solver yielding approximate reduced derivatives that can be used in an optimization iteration to change the design. The coordination of these three iterative processes is well established when only the state equation is considered as equality c...

This paper is devoted to the numerical resolution of unit-commitment problems, with emphasis on the French model optimizing the daily production of electricity. The solution process has two phases. First a Lagrangian relaxation solves the dual to find a lower bound; it also gives a primal relaxed solution. We then propose to use the latter in the second phase, for a heuristic resolution based o...

We consider the global and local convergence properties of a class of augmented La-grangian methods for solving nonlinear programming problems. In these methods, linear and more general constraints are handled in diierent ways. The general constraints are combined with the objective function in an augmented Lagrangian. The iteration consists of solving a sequence of sub-problems; in each subpro...

We define an interconnection network AQn,k which we call the augmented kary n-cube by extending a k-ary n-cube in a manner analogous to the existing extension of an n-dimensional hypercube to an n-dimensional augmented cube. We prove that the augmented k-ary n-cube AQn,k has a number of attractive properties (in the context of parallel computing). For example, we show that the augmented k-ary n...