COMPREHENSIVE COVERAGE OF NONLINEAR PROGRAMMING THEORY AND ALGORITHMS, THOROUGHLY REVISED AND EXPANDED"Nonlinear Programming: Theory and Algorithms"--now in an extensively updated Third Edition--addresses the problem of optimizing an objective function in the presence of equality and inequality constraints. Many realistic problems cannot be adequately represented as a linear program owing to the nature of the nonlinearity of the objective function and/or the nonlinearity of any constraints. The "Third Edition" begins with a general introduction to nonlinear programming with illustrative examples and guidelines for model construction.Concentration on the three major parts of nonlinear programming is provided: Convex analysis with discussion of topological properties of convex sets, separation and support of convex sets, polyhedral sets, extreme points and extreme directions of polyhedral sets, and linear programmingOptimality conditions and duality with coverage of the nature, interpretation, and value of the classical Fritz John (FJ) and the Karush-Kuhn-Tucker (KKT) optimality conditions; the interrelationships between various proposed constraint qualifications; and Lagrangian duality and saddle point optimality conditionsAlgorithms and their convergence, with a presentation of algorithms for solving both unconstrained and constrained nonlinear programming problemsImportant features of the "Third Edition" include: New topics such as second interior point methods, nonconvex optimization, nondifferentiable optimization, and moreUpdated discussion and new applications in each chapterDetailed numerical examples and graphical illustrationsEssential coverage of modeling and formulating nonlinear programsSimple numerical problemsAdvanced theoretical exercisesThe book is a solid reference for professionals as well as a useful text for students in the fields of operations research, management science, industrial engineering, applied mathematics, and also in engineering disciplines that deal with analytical optimization techniques. The logical and self-contained format uniquely covers nonlinear programming techniques with a great depth of information and an abundance of valuable examples and illustrations that showcase the most current advances in nonlinear problems.
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- Nazemi A and Dehghan M (2015). A neural network method for solving support vector classification problems, Neurocomputing, 152:C, (369-376), Online publication date: 25-Mar-2015.
- Scherrer A, Yaneva F, Grebe T and Küfer K (2015). A new mathematical approach for handling DVH criteria in IMRT planning, Journal of Global Optimization, 61:3, (407-428), Online publication date: 1-Mar-2015.
- Zhao K and Yang X (2015). Characterizations of efficient and weakly efficient points in nonconvex vector optimization, Journal of Global Optimization, 61:3, (575-590), Online publication date: 1-Mar-2015.
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- Zhai S, Xia T and Wang S A multi-class boosting method with direct optimization Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, (273-282)
- Wang Q, Si L, Zhang Z and Zhang N Active hashing with joint data example and tag selection Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, (405-414)
- Liu J, Xia C, Shroff N and Sherali H (2014). Distributed optimal load shedding for disaster recovery in smart electric power grids, ACM SIGMETRICS Performance Evaluation Review, 42:1, (591-592), Online publication date: 20-Jun-2014.
- Liu J, Xia C, Shroff N and Sherali H Distributed optimal load shedding for disaster recovery in smart electric power grids The 2014 ACM international conference on Measurement and modeling of computer systems, (591-592)
- Shi D, Chen T and Shi L (2014). Event-triggered maximum likelihood state estimation, Automatica (Journal of IFAC), 50:1, (247-254), Online publication date: 1-Jan-2014.
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- Gao H, Wang X, Tang J and Liu H Network denoising in social media Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, (564-571)
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- Liu J, Xia C, Shroff N and Zhang X (2013). On distributed computation rate optimization for deploying cloud computing programming frameworks, ACM SIGMETRICS Performance Evaluation Review, 40:4, (63-72), Online publication date: 29-Apr-2013.
- Mohammad Nezhad A and Mahlooji H (2011). A revised particle swarm optimization based discrete Lagrange multipliers method for nonlinear programming problems, Computers and Operations Research, 38:8, (1164-1174), Online publication date: 1-Aug-2011.
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- De Sterck H, Miller K and Sanders G (2011). Iterant recombination with one-norm minimization for multilevel Markov chain algorithms via the ellipsoid method, Computing and Visualization in Science, 14:2, (51-65), Online publication date: 1-Feb-2011.
- Yamada S, Tanaka T and Tanino T (2010). Outer Approximation Method Incorporating a Quadratic Approximation for a DC Programming Problem, Journal of Optimization Theory and Applications, 144:1, (156-183), Online publication date: 1-Jan-2010.
- Jia Liu , Tae Yoon Park , Hou Y, Yi Shi and Sherali H Cross-Layer Optimization of MIMO-Based Mesh Networks Under Orthogonal Channels Proceedings of the 2007 IEEE Wireless Communications and Networking Conference, (49-54)
- Yu C, Srinivasan D and Wang Q Global optimization through randomized group search in contracting regions 2016 IEEE Congress on Evolutionary Computation (CEC), (2813-2820)
- Chen J and Lau H Transferring autonomous reaching and targeting behaviors for cable-driven robots in minimally invasive surgery 2016 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), (79-84)
Index Terms
- Nonlinear Programming: Theory and Algorithms
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