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Mathematics of optimization

Optimization Theory is an active area of research with numerous applications; many of the books are designed for engineering classes, and thus have an emphasis on problems from such fields. Covering much of the same material, there is less emphasis on coding and detailed applications as the intended...

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Autor principal: Miller, Steven J
Lenguaje:eng
Publicado: American Mathematical Society 2017
Materias:
Acceso en línea:http://cds.cern.ch/record/2309150
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author Miller, Steven J
author_facet Miller, Steven J
author_sort Miller, Steven J
collection CERN
description Optimization Theory is an active area of research with numerous applications; many of the books are designed for engineering classes, and thus have an emphasis on problems from such fields. Covering much of the same material, there is less emphasis on coding and detailed applications as the intended audience is more mathematical. There are still several important problems discussed (especially scheduling problems), but there is more emphasis on theory and less on the nuts and bolts of coding. A constant theme of the text is the "why" and the "how" in the subject. Why are we able to do a calculation efficiently? How should we look at a problem? Extensive effort is made to motivate the mathematics and isolate how one can apply ideas/perspectives to a variety of problems. As many of the key algorithms in the subject require too much time or detail to analyze in a first course (such as the run-time of the Simplex Algorithm), there are numerous comparisons to simpler algorithms which students have either seen or can quickly learn (such as the Euclidean algorithm) to motivate the type of results on run-time savings.
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spelling cern-23091502021-04-21T18:52:59Zhttp://cds.cern.ch/record/2309150engMiller, Steven JMathematics of optimizationMathematical Physics and MathematicsOptimization Theory is an active area of research with numerous applications; many of the books are designed for engineering classes, and thus have an emphasis on problems from such fields. Covering much of the same material, there is less emphasis on coding and detailed applications as the intended audience is more mathematical. There are still several important problems discussed (especially scheduling problems), but there is more emphasis on theory and less on the nuts and bolts of coding. A constant theme of the text is the "why" and the "how" in the subject. Why are we able to do a calculation efficiently? How should we look at a problem? Extensive effort is made to motivate the mathematics and isolate how one can apply ideas/perspectives to a variety of problems. As many of the key algorithms in the subject require too much time or detail to analyze in a first course (such as the run-time of the Simplex Algorithm), there are numerous comparisons to simpler algorithms which students have either seen or can quickly learn (such as the Euclidean algorithm) to motivate the type of results on run-time savings.American Mathematical Societyoai:cds.cern.ch:23091502017
spellingShingle Mathematical Physics and Mathematics
Miller, Steven J
Mathematics of optimization
title Mathematics of optimization
title_full Mathematics of optimization
title_fullStr Mathematics of optimization
title_full_unstemmed Mathematics of optimization
title_short Mathematics of optimization
title_sort mathematics of optimization
topic Mathematical Physics and Mathematics
url http://cds.cern.ch/record/2309150
work_keys_str_mv AT millerstevenj mathematicsofoptimization