03966nam a22005655i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003100137050001800168072001600186072002300202082001400225100002300239245008600262264003800348300003500386336002600421337002600447338003600473347002400509490008700533505055100620520144501171650001702616650002502633650002102658650003502679650002502714650003102739650002402770650001702794650001802811650004002829650004502869650005402914650003002968650004302998700002803041710003403069773002003103776003603123830008703159856004403246912001403290999001903304952007703323978-0-387-32995-6DE-He21320180115171401.0cr nn 008mamaa100301s2006 xxu| s |||| 0|eng d a97803873299569978-0-387-32995-67 a10.1007/0-387-32995-12doi 4aQA402.5-402.6 7aPBU2bicssc 7aMAT0030002bisacsh04a519.62231 aLi, Duan.eauthor.10aNonlinear Integer Programmingh[electronic resource] /cby Duan Li, Xiaoling Sun. 1aBoston, MA :bSpringer US,c2006. aXXII, 438 p.bonline resource. atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier atext filebPDF2rda1 aInternational Series in Operations Research & Management Science,x0884-8289 ;v840 aOptimality, Relaxation and General Solution Procedures -- Lagrangian Duality Theory -- Surrogate Duality Theory -- Nonlinear Lagrangian and Strong Duality -- Nonlinear Knapsack Problems -- Separable Integer Programming -- Nonlinear Integer Programming with a Quadratic Objective Function -- Nonseparable Integer Programming -- Unconstrained Polynomial 0–1 Optimization -- Constrained Polynomial 0–1 Programming -- Two Level Methods for Constrained Polynomial 0–1 Programming -- Mixed-Integer Nonlinear Programming -- Global Descent Methods. aIt is not an exaggeration that much of what people devote in their hfe re solves around optimization in one way or another. On one hand, many decision making problems in real applications naturally result in optimization problems in a form of integer programming. On the other hand, integer programming has been one of the great challenges for the optimization research community for many years, due to its computational difficulties: Exponential growth in its computational complexity with respect to the problem dimension. Since the pioneering work of R. Gomory [80] in the late 1950s, the theoretical and methodological development of integer programming has grown by leaps and bounds, mainly focusing on linear integer programming. The past few years have also witnessed certain promising theoretical and methodological achieve ments in nonlinear integer programming. When the first author of this book was working on duality theory for n- convex continuous optimization in the middle of 1990s, Prof. Douglas J. White suggested that he explore an extension of his research results to integer pro gramming. The two authors of the book started their collaborative work on integer programming and global optimization in 1997. The more they have investigated in nonlinear integer programming, the more they need to further delve into the subject. Both authors have been greatly enjoying working in this exciting and challenging field. 0aMathematics. 0aOperations research. 0aDecision making. 0aComputer sciencexMathematics. 0aMathematical models. 0aMathematical optimization. 0aManagement science.14aMathematics.24aOptimization.24aOperation Research/Decision Theory.24aOperations Research, Management Science.24aMathematical Modeling and Industrial Mathematics.24aMathematics of Computing.24aMath Applications in Computer Science.1 aSun, Xiaoling.eauthor.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387295039 0aInternational Series in Operations Research & Management Science,x0884-8289 ;v8440uhttp://dx.doi.org/10.1007/0-387-32995-1 aZDB-2-SMA c369434d369434 001040708EBookaelibbelibd2018-01-15r2018-01-15w2018-01-15yEBOOK