A MATHEMATICAL VIEW OF INTERIOR-POINT METHODS IN CONVEX OPTIMIZATION
A Mathematical View of Interior-Point Methods in Convex Optimization
Society for Industrial Mathematics; 1st edition | Jan 1, 1987 | ISBN-10: 0898715024 | 118 pages | DJVU | 1.71 mb
This auto book, finished the simplifying appearance it presents, module verify a reverend who knows lowercase of interior-point methods to within range of the investigate frontier, nonindustrial key ideas that were over a decennium in the making by numerous interior-point method researchers. It aims at nonindustrial a complete discernment of the most generalized theory for interior-point methods, a collection of algorithms for lentiform improvement problems. The think of these algorithms has submissive the constant improvement literature for nearly 15 years. In that time, the theory has developed tremendously, but much of the literature is arduous to understand, modify for specialists. By centering exclusive on primary elements of the theory and action the inexplicit geometry, A Mathematical View of Interior-Point Methods in Convex Optimization makes the theory reachable to a panoramic audience, allowing them to apace amend a base discernment of the material.
The communicator begins with a generalized show of touchable relevant to constant improvement theory, phrased so as to be pronto practical in nonindustrial interior-point method theory. This show is cursive in much a artefact that modify impelled Ph.D. students who hit never had a instruction on constant improvement crapper acquire decent impression to full see the deeper theory that follows. Renegar continues by nonindustrial the base interior-point method theory, with inflection on need and intuition. In the test chapter, he focuses on the relations between interior-point methods and categorisation theory, including a self-contained launching to Hellenic categorisation theory for conelike programming; an expedition of symmetric cones; and the utilization of the generalized theory of primal-dual algorithms for finding conelike planning improvement problems.
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