286 research outputs found
A max-plus finite element method for solving finite horizon deterministic optimal control problems
We introduce a max-plus analogue of the Petrov-Galerkin finite element
method, to solve finite horizon deterministic optimal control problems. The
method relies on a max-plus variational formulation, and exploits the
properties of projectors on max-plus semimodules. We obtain a nonlinear
discretized semigroup, corresponding to a zero-sum two players game. We give an
error estimate of order , for a
subclass of problems in dimension 1. We compare our method with a max-plus
based discretization method previously introduced by Fleming and McEneaney.Comment: 13 pages, 5 figure
The max-plus finite element method for solving deterministic optimal control problems: basic properties and convergence analysis
We introduce a max-plus analogue of the Petrov-Galerkin finite element method
to solve finite horizon deterministic optimal control problems. The method
relies on a max-plus variational formulation. We show that the error in the sup
norm can be bounded from the difference between the value function and its
projections on max-plus and min-plus semimodules, when the max-plus analogue of
the stiffness matrix is exactly known. In general, the stiffness matrix must be
approximated: this requires approximating the operation of the Lax-Oleinik
semigroup on finite elements. We consider two approximations relying on the
Hamiltonian. We derive a convergence result, in arbitrary dimension, showing
that for a class of problems, the error estimate is of order or , depending on the
choice of the approximation, where and are respectively the
time and space discretization steps. We compare our method with another
max-plus based discretization method previously introduced by Fleming and
McEneaney. We give numerical examples in dimension 1 and 2.Comment: 31 pages, 11 figure
Bundle-based pruning in the max-plus curse of dimensionality free method
Recently a new class of techniques termed the max-plus curse of
dimensionality-free methods have been developed to solve nonlinear optimal
control problems. In these methods the discretization in state space is avoided
by using a max-plus basis expansion of the value function. This requires
storing only the coefficients of the basis functions used for representation.
However, the number of basis functions grows exponentially with respect to the
number of time steps of propagation to the time horizon of the control problem.
This so called "curse of complexity" can be managed by applying a pruning
procedure which selects the subset of basis functions that contribute most to
the approximation of the value function. The pruning procedures described thus
far in the literature rely on the solution of a sequence of high dimensional
optimization problems which can become computationally expensive.
In this paper we show that if the max-plus basis functions are linear and the
region of interest in state space is convex, the pruning problem can be
efficiently solved by the bundle method. This approach combining the bundle
method and semidefinite formulations is applied to the quantum gate synthesis
problem, in which the state space is the special unitary group (which is
non-convex). This is based on the observation that the convexification of the
unitary group leads to an exact relaxation. The results are studied and
validated via examples
Curse of dimensionality reduction in max-plus based approximation methods: theoretical estimates and improved pruning algorithms
Max-plus based methods have been recently developed to approximate the value
function of possibly high dimensional optimal control problems. A critical step
of these methods consists in approximating a function by a supremum of a small
number of functions (max-plus "basis functions") taken from a prescribed
dictionary. We study several variants of this approximation problem, which we
show to be continuous versions of the facility location and -center
combinatorial optimization problems, in which the connection costs arise from a
Bregman distance. We give theoretical error estimates, quantifying the number
of basis functions needed to reach a prescribed accuracy. We derive from our
approach a refinement of the curse of dimensionality free method introduced
previously by McEneaney, with a higher accuracy for a comparable computational
cost.Comment: 8pages 5 figure
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