pFilling the need for an introductory book on linear programming that discusses the important ways to mitigate parameter uncertainty, bIntroduction to Linear Optimization and Extensions with MATLABR bprovides a concrete and intuitive yet rigorous introduction to modern linear optimization. In addition to fundamental topics, the book discusses current linear optimization technologies such as predictor-path following interior point methods for both linear and quadratic optimization as well as the inclusion of linear optimization of uncertainty i.e. stochastic programming with recourse and robust optimization. ppThe author introduces both stochastic programming and robust optimization as frameworks to deal with parameter uncertainty. The author's unusual approach--developing these topics in an introductory book--highlights their importance. Since most applications require decisions to be made in the face of uncertainty, the early introduction of these topics facilitates decision making in