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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.
Steven J. Miller, Williams College, Williamstown, MA.
Classical algorithms: Efficient multiplication, IEfficient multiplication, IIIntroduction to linear programming: Introduction to linear programmingThe canonical linear programming problemSymmetries and dualitiesBasic feasible and basic optimal solutionsThe simplex methodAdvanced linear programming: Integer programmingInteger optimizationMulti-objective and quadratic programmingThe traveling salesman problemIntroduction to stochastic linear programmingFixed point theorems: Introduction to fixed point theoremsContraction mapsSperner's lemmaBrouwer's fixed point theoremAdvanced topics: Gale-Shapley algorithmInterpolating functionsThe four color problemThe Kepler conjectureIndexBibliography.
