106,394 research outputs found
On the Convergence Time of a Natural Dynamics for Linear Programming
We consider a system of nonlinear ordinary differential equations for the solution of linear programming (LP) problems that was first proposed in the mathematical biology literature as a model for the foraging behavior of acellular slime mold Physarum polycephalum, and more recently considered as a method to solve LP instances. We study the convergence time of the continuous Physarum dynamics in the context of the linear programming problem, and derive a new time bound to approximate optimality that depends on the relative entropy between projected versions of the optimal point and of the initial point. The bound scales logarithmically with the LP cost coefficients and linearly with the inverse of the relative accuracy, establishing the efficiency of the dynamics for arbitrary LP instances with positive costs
Mixed integer predictive control and shortest path reformulation
Mixed integer predictive control deals with optimizing integer and real
control variables over a receding horizon. The mixed integer nature of controls
might be a cause of intractability for instances of larger dimensions. To
tackle this little issue, we propose a decomposition method which turns the
original -dimensional problem into indipendent scalar problems of lot
sizing form. Each scalar problem is then reformulated as a shortest path one
and solved through linear programming over a receding horizon. This last
reformulation step mirrors a standard procedure in mixed integer programming.
The approximation introduced by the decomposition can be lowered if we operate
in accordance with the predictive control technique: i) optimize controls over
the horizon ii) apply the first control iii) provide measurement updates of
other states and repeat the procedure
Singularly perturbed forward-backward stochastic differential equations: application to the optimal control of bilinear systems
We study linear-quadratic stochastic optimal control problems with bilinear
state dependence for which the underlying stochastic differential equation
(SDE) consists of slow and fast degrees of freedom. We show that, in the same
way in which the underlying dynamics can be well approximated by a reduced
order effective dynamics in the time scale limit (using classical
homogenziation results), the associated optimal expected cost converges in the
time scale limit to an effective optimal cost. This entails that we can well
approximate the stochastic optimal control for the whole system by the reduced
order stochastic optimal control, which is clearly easier to solve because of
lower dimensionality. The approach uses an equivalent formulation of the
Hamilton-Jacobi-Bellman (HJB) equation, in terms of forward-backward SDEs
(FBSDEs). We exploit the efficient solvability of FBSDEs via a least squares
Monte Carlo algorithm and show its applicability by a suitable numerical
example
- …