Temat: nonconvex optimization

Skocz do pozycji: 1.

Tytuł:: Optimization problems with convex epigraphs. Application to optimal control
Autorzy:: Kryazhimskii, A. V.
Tematy:: równanie nieliniowe
optymalizacja
nonconvex optimization
global optimization methods; Pokaż więcej
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:: https://bibliotekanauki.pl/articles/908078.pdf Link otwiera się w nowym oknie
Opis:: For a class of infinite-dimensional minimization problems with nonlinear equality constraints, an iterative algorithm for finding global solutions is suggested. A key assumption is the convexity of the "epigraph", a set in the product of the image spaces of the constraint and objective functions. A convexification method involving randomization is used. The algorithm is based on the extremal shift control principle due to N.N. Krasovskii. An application to a problem of optimal control for a bilinear control system is described.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 2.

Tytuł:: A Software Platform for Global Optimization
Autorzy:: Niewiadomska-Szynkiewicz, E.
Marks, M.
Tematy:: global optimization
integrated software systems
nonconvex optimization
numerical libraries
price management; Pokaż więcej
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Powiązania:: https://bibliotekanauki.pl/articles/308471.pdf Link otwiera się w nowym oknie
Opis:: This paper addresses issues associated with the global optimization algorithms, which are methods to find optimal solutions for given problems. It focuses on an integrated software environment - global optimization object-oriented library (GOOL), which provides the graphical user interface together with the library of solvers for convex and nonconvex, unconstrained and constrained problems. We describe the design, performance and possible applications of the GOOL system. The practical example - price management problem - is provided to illustrate the effectiveness and range of applications of our software tool.
Dostawca treści:: Biblioteka Nauki

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na półce

Skocz do pozycji: 3.

Tytuł:: Generalizing trade-off deirections in multiobjective optimization
Autorzy:: Mäkelä, M. M.
Nikulin, Y.
Mezei, J.
Tematy:: generalized trade-off directions
multiobjective optimization
geometrical characterization
convex and nonconvex optimization
optimality principles; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Instytut Badań Systemowych PAN
Powiązania:: https://bibliotekanauki.pl/articles/205690.pdf Link otwiera się w nowym oknie
Opis:: We consider a general multiobjective optimization problem with five basic optimality principles: efficiency, weak and proper Pareto optimality, strong efficiency and lexicographic optimality. We generalize the concept of trade-off directions defining them as some optimal surface of appropriate cones. In convex optimization, the contingent cone can be used for all optimality principles except lexicographic optimality, where the cone of feasible directions is useful. In nonconvex case the contingent cone and the cone of locally feasible directions with lexicographic optimality are helpful. We derive necessary and sufficient geometrical optima lity conditions in terms of corresponding trade-off directions for both convex and nonconvex cases.
Dostawca treści:: Biblioteka Nauki

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na półce

Skocz do pozycji: 4.

Tytuł:: Random perturbation of the variable metric method for unconstrained nonsmooth nonconvex optimization
Autorzy:: El Mouatasim, A.
Ellaia, R.
Souza de Cursi, J. E.
Tematy:: optymalizacja
zaburzenie stochastyczne
zaburzenie losowe
nonconvex optimization
stochastic perturbation
variable metric method
nonsmooth optimization
generalized gradient; Pokaż więcej
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:: https://bibliotekanauki.pl/articles/908374.pdf Link otwiera się w nowym oknie
Opis:: We consider the global optimization of a nonsmooth (nondifferentiable) nonconvex real function. We introduce a variable metric descent method adapted to nonsmooth situations, which is modified by the incorporation of suitable random perturbations. Convergence to a global minimum is established and a simple method for the generation of suitable perturbations is introduced. An algorithm is proposed and numerical results are presented, showing that the method is computationally effective and stable.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Localization in Wireless Sensor Networks Using Heuristic Optimization Techniques
Autorzy:: Niewiadomska-Szynkiewicz, E.
Marks, M.
Kamola, M.
Tematy:: evolutionary strategy
genetic algorithm
localization
location systems
nonconvex optimization
simulated annealing
wireless sensor network; Pokaż więcej
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Powiązania:: https://bibliotekanauki.pl/articles/308429.pdf Link otwiera się w nowym oknie
Opis:: Many applications of wireless sensor networks (WSN) require information about the geographic location of each sensor node. Devices that form WSN are expected to be remotely deployed in large numbers in a sensing field, and to self-organize to perform sensing and acting task. The goal of localization is to assign geographic coordinates to each device with unknown position in the deployment area. Recently, the popular strategy is to apply optimization algorithms to solve the localization problem. In this paper, we address issues associated with the application of heuristic techniques to accurate localization of nodes in a WSN system. We survey and discuss the location systems based on simulated annealing, genetic algorithms and evolutionary strategies. Finally, we describe and evaluate our methods that combine trilateration and heuristic optimization.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 6.

Tytuł:: Convergence of the Gradient Sampling Algorithm for Nonsmooth Nonconvex Optimization
Raport Badawczy = Research Report ; RB/53/2006
Autorzy:: Kiwiel, Krzysztof
Wydawca:: Instytut Badań Systemowych. Polska Akademia Nauk
Systems Research Institute. Polish Academy of Sciences
Powiązania:: Raport Badawczy = Research Report
Opis:: The paper investigates the gradient sampling algorithm of Burke, Lewis and Overton for minimizing a locally Lipschitz function f on Rn that is continuously differentiable on an open dense subset. The existing convergence results for this algorithm were reinforced. A slightly revised version has been introduced for which stronger results are established without requiring compactness of the level sets of f. In particular, it has been shown that with probability 1 the revised algorithm either drives the f -values to -∞, or each of its cluster points is Clarke stationary for f. A simplified variant was also considered in which the differentiability check is skipped and the user can control the number of f-evaluations per iteration.
Bibliografia s. 10-11
11 pages ; 21 cm
Bibliography p. 10-11
11 stron ; 21 cm
Dostawca treści:: RCIN - Repozytorium Cyfrowe Instytutów Naukowych

Książka

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Skocz do pozycji: 7.

Tytuł:: Convergence of the Gradient Sampling Algorithm for Nonsmooth Nonconvex Optimization
Raport Badawczy = Research Report ; RB/48/2005
Autorzy:: Kiwiel, Krzysztof
Wydawca:: Instytut Badań Systemowych. Polska Akademia Nauk
Systems Research Institute. Polish Academy of Sciences
Powiązania:: Raport Badawczy = Research Report
Opis:: Bibliografia s. 10-11
11 pages ; 21 cm
Bibliography p. 10-11
The paper deals with the gradient sampling algorithm of Burke, Lewis and Overton for minimizing a locally Lipschitz function f on Rn that is continuously differentiable on an open dense subset. The authors strengthened the existing convergence results for this algorithm, and introduce a slightly revised version for which stronger results are established without requiring compactness of the level sets of f. In particular, it has been shown that with probability 1 the revised algorithm either drives the f -values to -∞, or each of its cluster points is Clarke stationary for f. A simplified variant was also considered in which the differentiability check is skipped and the user can control the number of f -evaluations per iteration.
11 stron ; 21 cm
Dostawca treści:: RCIN - Repozytorium Cyfrowe Instytutów Naukowych

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Skocz do pozycji: 8.

Tytuł:: A nonderivative version of the gradient sampling algorithm for nonsmooth nonconvex optimization
Raport Badawczy = Research Report ; RB/35/2009
Autorzy:: Kiwiel, Krzysztof
Wydawca:: Instytut Badań Systemowych. Polska Akademia Nauk
Systems Research Institute. Polish Academy of Sciences
Powiązania:: Raport Badawczy = Research Report
Opis:: Bibliography p. 11-12
The article gives a nonderivative version of the gradient sampling algorithm of Burke, Lewis and Overton for minimizing a locally Lipschitz function f on Rn that is continuously differentiable on an open dense subset. Instead of gradients of f, estimates of gradients of the Steklov averages of f were used. It has been shown that the nonderivative version retains the convergence properties of the gradient sampling algorithm. In particular, with probability 1 it either drives the f-values to -∞ or each of its cluster points is Clarke stationary for f.
Bibliografia s. 11-12
12 stron ; 21 cm
12 pages ; 21 cm
Dostawca treści:: RCIN - Repozytorium Cyfrowe Instytutów Naukowych

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