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Optimization - Algorithms and Consistent Approximations (Paperback, Softcover reprint of the original 1st ed. 1997)
Loot Price: R8,217
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Optimization - Algorithms and Consistent Approximations (Paperback, Softcover reprint of the original 1st ed. 1997)
Series: Applied Mathematical Sciences, 124
Expected to ship within 10 - 15 working days
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This book deals with optimality conditions, algorithms, and
discretization tech niques for nonlinear programming, semi-infinite
optimization, and optimal con trol problems. The unifying thread in
the presentation consists of an abstract theory, within which
optimality conditions are expressed in the form of zeros of
optimality junctions, algorithms are characterized by point-to-set
iteration maps, and all the numerical approximations required in
the solution of semi-infinite optimization and optimal control
problems are treated within the context of con sistent
approximations and algorithm implementation techniques.
Traditionally, necessary optimality conditions for optimization
problems are presented in Lagrange, F. John, or Karush-Kuhn-Tucker
multiplier forms, with gradients used for smooth problems and
subgradients for nonsmooth prob lems. We present these classical
optimality conditions and show that they are satisfied at a point
if and only if this point is a zero of an upper semicontinuous
optimality junction. The use of optimality functions has several
advantages. First, optimality functions can be used in an abstract
study of optimization algo rithms. Second, many optimization
algorithms can be shown to use search directions that are obtained
in evaluating optimality functions, thus establishing a clear
relationship between optimality conditions and algorithms. Third,
estab lishing optimality conditions for highly complex problems,
such as optimal con trol problems with control and trajectory
constraints, is much easier in terms of optimality functions than
in the classical manner. In addition, the relationship between
optimality conditions for finite-dimensional problems and
semi-infinite optimization and optimal control problems becomes
transparent.
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