Linear Programming with MATLAB

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Bol Provides a self-contained introduction to linear programming using MATLAB® software to elucidate the development of algorithms and theory. Exercises are interwoven with the theory presented in each chapter, and two appendices provide additional information on linear algebra, convexity, and nonlinear functions and on available MATLAB commands, respectively. This textbook provides a self-contained introduction to linear programming using MATLAB® software to elucidate the development of algorithms and theory. Early chapters cover linear algebra basics, the simplex method, duality, the solving of large linear problems, sensitivity analysis, and parametric linear programming. In later chapters, the authors discuss quadratic programming, linear complementarity, interior-point methods, and selected applications of linear programming to approximation and classification problems. Exercises are interwoven with the theory presented in each chapter, and two appendices provide additional information on linear algebra, convexity, and nonlinear functions and on available MATLAB commands, respectively. Readers can access MATLAB codes and associated mex files at a Web site maintained by the authors.

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Provides a self-contained introduction to linear programming using MATLAB® software to elucidate the development of algorithms and theory. Exercises are interwoven with the theory presented in each chapter, and two appendices provide additional information on linear algebra, convexity, and nonlinear functions and on available MATLAB commands, respectively. This textbook provides a self-contained introduction to linear programming using MATLAB® software to elucidate the development of algorithms and theory. Early chapters cover linear algebra basics, the simplex method, duality, the solving of large linear problems, sensitivity analysis, and parametric linear programming. In later chapters, the authors discuss quadratic programming, linear complementarity, interior-point methods, and selected applications of linear programming to approximation and classification problems. Exercises are interwoven with the theory presented in each chapter, and two appendices provide additional information on linear algebra, convexity, and nonlinear functions and on available MATLAB commands, respectively. Readers can access MATLAB codes and associated mex files at a Web site maintained by the authors.


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