2 edition of **Near-optimal constrained image inversion using convex sets.** found in the catalog.

Near-optimal constrained image inversion using convex sets.

Peter John Scarlett

- 31 Want to read
- 2 Currently reading

Published
**1987**
.

Written in English

The Physical Object | |
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Pagination | [76] leaves |

Number of Pages | 76 |

ID Numbers | |

Open Library | OL18079305M |

Stationarity in Convex Optimization For convex problems, stationarity is a necessary and su cient condition f be a continuously di erentiable convex function over a nonempty closed and convex set C Rn. Then x is a stationary point of (P) min f(x) s.t. x 2C: i . In this work, we consider a bilevel optimization problem consisting of the minimizing sum of two convex functions in which one of them is a composition of a convex function and a nonzero linear transformation subject to the set of all feasible points represented in the form of common fixed-point sets of nonlinear operators. To find an optimal solution to the problem, we present a fixed-point Author: Nimit Nimana.

4 Chapter 1. Cooperative Distributed Multi-Agent Optimization Figure Multiagent cooperative optimization problem. where T: Rm! Ris an increasing convex function.1 The decision vector x is constrained to lie in a set, x 2 C, which is a combination of local constraints and additional global constraints that may be imposed by the network structure, i.e.,File Size: KB. Without a doubt Boyd & Vandenberghe is the standard introduction at the graduate level. Anybody who’s serious about understanding convex optimization must engage with it. However, it’s a fairly difficult book, and you have to have a pretty good ma.

CONVEX CONSTRAINED OPTIMIZATION PROBLEMS 45 (1) The optimal value f∗ is ﬁnite. (2) The optimal set X∗ is nonempty. (3) If Ay ≤ 0 and Py = 0 for some y ∈ Rn, then cTy ≥ 0. The proof of Theorem 18 requires the notion of recession directions of convex closed sets. Maximizing a convex function (minimizing a concave function) with a linear constraint. Ask Question A method for globally minimizing convex functions over convex sets, Mathematical Programming, , Vol. 20, p. W in New York phone book mean?

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So that's worth an extra star right there. I learned convex optimization out of this book, and I use it as a reference. In particular, I like chapter 3 on convex functions, and chapter 2 on convex sets/5.

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