Discrete approximations for strict convex continuous time problems and duality
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Abstract
We propose a discrete approximation scheme to a class of Linear Quadratic Continuous Time Problems. It is shown, under positiveness of the matrix in the integral cost, that optimal solutions of the discrete problems provide a sequence of bounded variation functions which converges almost everywhere to the unique optimal solution. Furthermore, the method of discretization allows us to derive a number of interesting results based on finite dimensional optimization theory, namely, Karush-Kuhn-Tucker conditions of optimality and weak and strong duality. A number of examples are provided to illustrate the theory.
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Continuous time optimization, Discrete approximation, Linear Quadratic problems, Strict convexity
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English
Citation
Computational and Applied Mathematics, v. 23, n. 1, p. 81-105, 2004.
