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Unconstrained Optimization

  • Elijah Polak
Part of the Applied Mathematical Sciences book series (AMS, volume 124)

Abstract

We devote this chapter primarily to optimality conditions and algorithms for solving unconstrained optimization problems of the form min x ∈ IR n f(x), where f(•) is a continuously differentiable cost function defined on IR n . When convenient, we will extend our results to problems of the form min x X f(x), where f (•) is a continuously differentiable cost function, defined on IR n , and X ⊂ IR n is an “unstructured”, convex, constraint set. We expect the reader to be familiar with the mathematical background contained in the first four sections of Chapter 5.

Keywords

Search Direction Conjugate Gradient Method Accumulation Point Algorithm Model Conjugate Gradient Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Elijah Polak
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of CaliforniaBerkeleyUSA

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