@article{1440779,
      recid = {1440779},
      author = {Luenberger, David G., and Ye, Yinyu,},
      title = {Linear and nonlinear programming /},
      pages = {1 online resource :},
      abstract = {The 5th edition of this classic textbook covers the  central concepts of practical optimization techniques, with  an emphasis on methods that are both state-of-the-art and  popular. One major insight is the connection between the  purely analytical character of an optimization problem and  the behavior of algorithms used to solve that problem.  End-of-chapter exercises are provided for all chapters. The  material is organized into three separate parts. Part I  offers a self-contained introduction to linear programming.  The presentation in this part is fairly conventional,  covering the main elements of the underlying theory of  linear programming, many of the most effective numerical  algorithms, and many of its important special applications.  Part II, which is independent of Part I, covers the theory  of unconstrained optimization, including both derivations  of the appropriate optimality conditions and an  introduction to basic algorithms. This part of the book  explores the general properties of algorithms and defines  various notions of convergence. In turn, Part III extends  the concepts developed in the second part to constrained  optimization problems. Except for a few isolated sections,  this part is also independent of Part I. As such, Parts II  and III can easily be used without reading Part I and, in  fact, the book has been used in this way at many  universities. New to this edition are popular topics in  data science and machine learning, such as the Markov  Decision Process, Farkas' lemma, convergence speed  analysis, duality theories and applications, various  first-order methods, stochastic gradient method,  mirror-descent method, Frank-Wolf method, ALM/ADMM method,  interior trust-region method for non-convex optimization,  distributionally robust optimization, online linear  programming, semidefinite programming for sensor-network  localization, and infeasibility detection for nonlinear  optimization.},
      url = {http://library.usi.edu/record/1440779},
      doi = {https://doi.org/10.1007/978-3-030-85450-8},
}