48,228 research outputs found
Finite dimensional approximation in infinite dimensional mathematical programming
We consider the problem of approximating an optimal solution to a separable, doubly infinite mathematical program (P) with lower staircase structure by solutions to the programs (P( N )) obtained by truncating after the first N variables and N constraints of (P). Viewing the surplus vector variable associated with the N th constraint as a state, and assuming that all feasible states are eventually reachable from any feasible state, we show that the efficient set of all solutions optimal to all possible feasible surplus states for (P( N )) converges to the set of optimal solutions to (P). A tie-breaking algorithm which selects a nearest-point efficient solution for (P( N )) is shown (for convex programs) to converge to an optimal solution to (P). A stopping rule is provided for discovering a value of N sufficiently large to guarantee any prespecified level of accuracy. The theory is illustrated by an application to production planning.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47924/1/10107_2005_Article_BF01586057.pd
A method for pricing American options using semi-infinite linear programming
We introduce a new approach for the numerical pricing of American options.
The main idea is to choose a finite number of suitable excessive functions
(randomly) and to find the smallest majorant of the gain function in the span
of these functions. The resulting problem is a linear semi-infinite programming
problem, that can be solved using standard algorithms. This leads to good upper
bounds for the original problem. For our algorithms no discretization of space
and time and no simulation is necessary. Furthermore it is applicable even for
high-dimensional problems. The algorithm provides an approximation of the value
not only for one starting point, but for the complete value function on the
continuation set, so that the optimal exercise region and e.g. the Greeks can
be calculated. We apply the algorithm to (one- and) multidimensional diffusions
and to L\'evy processes, and show it to be fast and accurate
From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming
We consider linear programming (LP) problems in infinite dimensional spaces
that are in general computationally intractable. Under suitable assumptions, we
develop an approximation bridge from the infinite-dimensional LP to tractable
finite convex programs in which the performance of the approximation is
quantified explicitly. To this end, we adopt the recent developments in two
areas of randomized optimization and first order methods, leading to a priori
as well as a posterior performance guarantees. We illustrate the generality and
implications of our theoretical results in the special case of the long-run
average cost and discounted cost optimal control problems for Markov decision
processes on Borel spaces. The applicability of the theoretical results is
demonstrated through a constrained linear quadratic optimal control problem and
a fisheries management problem.Comment: 30 pages, 5 figure
Approximations of countably-infinite linear programs over bounded measure spaces
We study a class of countably-infinite-dimensional linear programs (CILPs)
whose feasible sets are bounded subsets of appropriately defined weighted
spaces of measures. We show how to approximate the optimal value, optimal
points, and minimal points of these CILPs by solving finite-dimensional linear
programs. The errors of our approximations converge to zero as the size of the
finite-dimensional program approaches that of the original problem and are easy
to bound in practice. We discuss the use of our methods in the computation of
the stationary distributions, occupation measures, and exit distributions of
Markov~chains
Optimal Stabilization using Lyapunov Measures
Numerical solutions for the optimal feedback stabilization of discrete time
dynamical systems is the focus of this paper. Set-theoretic notion of almost
everywhere stability introduced by the Lyapunov measure, weaker than
conventional Lyapunov function-based stabilization methods, is used for optimal
stabilization. The linear Perron-Frobenius transfer operator is used to pose
the optimal stabilization problem as an infinite dimensional linear program.
Set-oriented numerical methods are used to obtain the finite dimensional
approximation of the linear program. We provide conditions for the existence of
stabilizing feedback controls and show the optimal stabilizing feedback control
can be obtained as a solution of a finite dimensional linear program. The
approach is demonstrated on stabilization of period two orbit in a controlled
standard map
Fast algorithms for solving H∞-norm minimization problems
We propose an efficient computational approach to minimize the H ∞-norm of a transfer-function matrix depending affinely on a set of free parameters. The minimization problem, formulated as a semi-infinite convex programming problem, is solved via a relaxation approach over a finite set of frequency values. In this way, a significant speed up is achieved by avoiding the solution of high order LMIs resulting by equivalently formulating the minimization problem as a high dimensional semidefinite programming problem. Numerical results illustrate the superiority of proposed approach over LMIs based techniques in solving zero order H∞-norm approximation problems
Approximate Dynamic Programming via Sum of Squares Programming
We describe an approximate dynamic programming method for stochastic control
problems on infinite state and input spaces. The optimal value function is
approximated by a linear combination of basis functions with coefficients as
decision variables. By relaxing the Bellman equation to an inequality, one
obtains a linear program in the basis coefficients with an infinite set of
constraints. We show that a recently introduced method, which obtains convex
quadratic value function approximations, can be extended to higher order
polynomial approximations via sum of squares programming techniques. An
approximate value function can then be computed offline by solving a
semidefinite program, without having to sample the infinite constraint. The
policy is evaluated online by solving a polynomial optimization problem, which
also turns out to be convex in some cases. We experimentally validate the
method on an autonomous helicopter testbed using a 10-dimensional helicopter
model.Comment: 7 pages, 5 figures. Submitted to the 2013 European Control
Conference, Zurich, Switzerlan
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