## Optimality in nonlinear programming: a feasible directions approachGood,No Highlights,No Markup,all pages are intact, Slight Shelfwear,may have the corners slightly dented, may have slight color changes/slightly damaged spine. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

CONVEX PROGRAMMING | 1 |

Characterizing Optimal Solutions | 13 |

Some Computational Methods | 59 |

Copyright | |

4 other sections not shown

### Common terms and phrases

Algorithm 3.9 BEN-ISRAEL BEN-TAL binding constraints characterization Chebyshev solution computational constraint qualification contradicting convex cone convex program convex set denote direction of descent directions of constancy DNLP Dubovitskii-Milyutin equivalent Euler-Lagrange equation Exercises and Examples exists a vector faithfully convex functions feasible direction methods feasible point feasible set feasible solution fk(x Fritz John G Df(x GIRSANOV 72 Hence implies inconsistent index set inequality iteration John condition Kf(x Kuhn-Tucker condition Lemma lexicographic linear program linearly independent MANGASARIAN matrix MELP multipliers necessary condition non-negative nonascent nonempty nonlinear programming optimal solution Pareto minimum Pareto optimality posynomial primal problem Proof pseudoconvex quasiconvex quasiconvex functions quasiusable direction reduction condition relevant intervals satisfying scalars second-order conditions Section separation theorem set of binding Slater's condition subset ft sufficient condition sush thst TH(x Theorem 3.2 tions usable directions zero ZLOBEC