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# Balanced treatment of the simplex method and interior-point methods. # Efficient source code (in C) for all the algorithms presented in the text. # Thorough discussion of several interior-point methods including primal-dual path-following, affine-scaling, and homogeneous self dual methods. # Extensive coverage of applications including traditional topics such as network flows and game theory as well as less familiar ones such as structural optimization, L^1 regression, and the Markowitz portfolio optimization model. # Over 200 class-tested exercises. # A dynamically expanding collection of exercises.

The leading textbook in Artificial Intelligence. Used in over 1000 universities in 91 countries (over 90% market share). The 85th most cited publication on Citeseer.

The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online.