223 research outputs found
Nonlocal error bounds for piecewise affine functions
The paper is devoted to a detailed analysis of nonlocal error bounds for
nonconvex piecewise affine functions. We both improve some existing results on
error bounds for such functions and present completely new necessary and/or
sufficient conditions for a piecewise affine function to have an error bound on
various types of bounded and unbounded sets. In particular, we show that any
piecewise affine function has an error bound on an arbitrary bounded set and
provide several types of easily verifiable sufficient conditions for such
functions to have an error bound on unbounded sets. We also present general
necessary and sufficient conditions for a piecewise affine function to have an
error bound on a finite union of polyhedral sets (in particular, to have a
global error bound), whose derivation reveals a structure of sublevel sets and
recession functions of piecewise affine functions
Inégalités de Kurdyka-Lojasiewicz et convexité : algorithmes et applications
Cette thèse traite des méthodes de descente d’ordre un pour les problèmes de minimisation. Elle comprend trois parties. Dans la première partie, nous apportons une vue d’ensemble des bornes d’erreur et les premières briques d’unification d’un concept. Nous montrons en effet la place centrale de l’inégalité du gradient de Lojasiewicz, en mettant en relation cette inégalité avec les bornes d’erreur. Dans la seconde partie, en usant de l’inégalité de Kurdyka-Lojasiewicz (KL), nous apportons un nouvel outil pour calculer la complexité des m´méthodes de descente d’ordre un pour la minimisation convexe. Notre approche est totalement originale et utilise une suite proximale “worst-case” unidimensionnelle. Ces résultats introduisent une méthodologie simple : trouver une borne d’erreur, calculer la fonction KL désingularisante quand c’est possible, identifier les constantes pertinentes dans la méthode de descente, et puis calculer la complexité en usant de la suite proximale “worst-case” unidimensionnelle. Enfin, nous étendons la méthode extragradient pour minimiser la somme de deux fonctions, la première étant lisse et la seconde convexe. Sous l’hypothèse de l’inégalité KL, nous montrons que la suite produite par la méthode extragradient converge vers un point critique de ce problème et qu’elle est de longueur finie. Quand les deux fonctions sont convexes, nous donnons la vitesse de convergence O(1/k) qui est classique pour la méthode de gradient. De plus, nous montrons que notre complexité de la seconde partie peut être appliquée à cette méthode. Considérer la méthode extragradient est l’occasion de d´écrire la recherche linéaire exacte pour les méthodes de décomposition proximales. Nous donnons des détails pour l’implémentation de ce programme pour le problème des moindres carrés avec régularisation ℓ1 et nous donnons des résultats numériques qui suggèrent que combiner des méthodes non-accélérées avec la recherche linéaire exacte peut être un choix performant.This thesis focuses on first order descent methods in the minimization problems. There are three parts. Firstly, we give an overview on local and global error bounds. We try to provide the first bricks of a unified theory by showing the centrality of the Lojasiewicz gradient inequality. In the second part, by using Kurdyka- Lojasiewicz (KL) inequality, we provide new tools to compute the complexity of first-order descent methods in convex minimization. Our approach is completely original and makes use of a one-dimensional worst-case proximal sequence. This result inaugurates a simple methodology: derive an error bound, compute the KL esingularizing function whenever possible, identify essential constants in the descent method and finally compute the complexity using the one-dimensional worst case proximal sequence. Lastly, we extend the extragradient method to minimize the sum of two functions, the first one being smooth and the second being convex. Under Kurdyka-Lojasiewicz assumption, we prove that the sequence produced by the extragradient method converges to a critical point of this problem and has finite length. When both functions are convex, we provide a O(1/k) convergence rate. Furthermore, we show that our complexity result in the second part can be applied to this method. Considering the extragradient method is the occasion to describe exact line search for proximal decomposition methods. We provide details for the implementation of this scheme for the ℓ1 regularized least squares problem and give numerical results which suggest that combining nonaccelerated methods with exact line search can be a competitive choice
Fitting piecewise linear continuous functions
Abstract We consider the problem of fitting a continuous piecewise linear function to a finite set of data points, modeled as a mathematical program with convex objective. We review some fitting problems that can be modeled as convex programs, and then introduce mixed-binary generalizations that allow variability in the regions defining the best-fit function's domain. We also study the additional constraints required to impose convexity on the best-fit function
Continuous Multiclass Labeling Approaches and Algorithms
We study convex relaxations of the image labeling problem on a continuous
domain with regularizers based on metric interaction potentials. The generic
framework ensures existence of minimizers and covers a wide range of
relaxations of the originally combinatorial problem. We focus on two specific
relaxations that differ in flexibility and simplicity -- one can be used to
tightly relax any metric interaction potential, while the other one only covers
Euclidean metrics but requires less computational effort. For solving the
nonsmooth discretized problem, we propose a globally convergent
Douglas-Rachford scheme, and show that a sequence of dual iterates can be
recovered in order to provide a posteriori optimality bounds. In a quantitative
comparison to two other first-order methods, the approach shows competitive
performance on synthetical and real-world images. By combining the method with
an improved binarization technique for nonstandard potentials, we were able to
routinely recover discrete solutions within 1%--5% of the global optimum for
the combinatorial image labeling problem
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