Titlebook: Linear and Nonlinear Optimization; Richard W. Cottle,Mukund N. Thapa Textbook 2017 Springer Science+Business Media LLC 2017 Operations Res

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POSTOPTIMALITY ANALYSES,By . we mean various types of analyses that can be performed after an optimal basic feasible solution for an LP has been found. In practice it is usually not enough just to find an optimal solution. Often questions are asked relating to the impact of potential data changes on the optimal solution.

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NLP MODELS AND APPLICATIONS,We now turn to the study of ., also known as .. In this chapter we discuss the differences between linear and nonlinear programming and state the form of the general nonlinear optimization model. We will see that in contrast to linear programming, it . make sense to talk about unconstrained nonlinear optimization problems.

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OPTIMALITY CONDITIONS,This chapter is concerned with material that is central to the theory of nonlinear optimization: the conditions (equations and inequalities) that must hold at a local minimizer of a (smooth) nonlinear optimization problem. These are called ..

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PROBLEMS WITH LINEAR CONSTRAINTS,The optimization methods presented in this chapter are for solving the important class of nonlinear programs with linear constraints, that is, linear equations and/or linear inequalities. The algorithms covered here are based on ones designed for unconstrained optimization, but they are modified to take account of the constraints.

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