Documents  90C25 | enregistrements trouvés : 37

- x; 336 p.
ISBN 978-3-319-00199-9

Fields institute communications , 0069

Localisation : Collection 1er étage

géométrie discrète # optimisation

52A10 ; 52A21 ; 52A35 ; 52B11 ; 52C15 ; 52C17 ; 52C20 ; 52C35 ; 52C45 ; 90C05 ; 90C22 ; 90C25 ; 90C27 ; 90C34

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ISBN 978-3-540-57624-2

Lecture notes in economics and mathematical systems , 0405

Localisation : Colloque 1er étage (PECS)

application monotone généralisée # convexité généralisée # fonction convexe généralisée # optimalité et dualité # programmation fractionnaire # programmation multi-objectif # programmation quasi-convexe

52A41 ; 52Axx ; 90C25 ; 90C26 ; 90C29

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- xiii, 279 p.
ISBN 978-3-642-11338-3

Lecture notes in mathematics , 1989

Localisation : Collection 1er étage

optimisation # programmation non-linéaire # problème quadratique

90C30 ; 90C26 ; 90C25 ; 90C55

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Special events;30 Years of Wavelets;Analysis and its Applications;Probability and Statistics;Numerical Analysis and Scientific Computing

One of the important "products" of wavelet theory consists in the insight that it is often beneficial to consider sparsity in signal processing applications. In fact, wavelet compression relies on the fact that wavelet expansions of real-world signals and images are usually sparse. Compressive sensing builds on sparsity and tells us that sparse signals (expansions) can be recovered from incomplete linear measurements (samples) efficiently. This finding triggered an enormous research activity in recent years both in signal processing applications as well as their mathematical foundations. The present talk discusses connections of compressive sensing and time-frequency analysis (the sister of wavelet theory). In particular, we give on overview on recent results on compressive sensing with time-frequency structured random matrices.

Keywords: compressive sensing - time-frequency analysis - wavelets - sparsity - random matrices - $\ell_1$-minimization - radar - wireless communications One of the important "products" of wavelet theory consists in the insight that it is often beneficial to consider sparsity in signal processing applications. In fact, wavelet compression relies on the fact that wavelet expansions of real-world signals and images are usually sparse. Compressive sensing builds on sparsity and tells us that sparse signals (expansions) can be recovered from incomplete linear measurements (samples) efficiently. This ...

94A20 ; 94A08 ; 42C40 ; 60B20 ; 90C25

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Research talks;Computer Science;Control Theory and Optimization;Probability and Statistics

Many machine learning and signal processing problems are traditionally cast as convex optimization problems. A common difficulty in solving these problems is the size of the data, where there are many observations ("large n") and each of these is large ("large p"). In this setting, online algorithms such as stochastic gradient descent which pass over the data only once, are usually preferred over batch algorithms, which require multiple passes over the data. Given n observations/iterations, the optimal convergence rates of these algorithms are $O(1/\sqrt{n})$ for general convex functions and reaches $O(1/n)$ for strongly-convex functions. In this tutorial, I will first present the classical results in stochastic approximation and relate them to classical optimization and statistics results. I will then show how the smoothness of loss functions may be used to design novel algorithms with improved behavior, both in theory and practice: in the ideal infinite-data setting, an efficient novel Newton-based stochastic approximation algorithm leads to a convergence rate of $O(1/n)$ without strong convexity assumptions, while in the practical finite-data setting, an appropriate combination of batch and online algorithms leads to unexpected behaviors, such as a linear convergence rate for strongly convex problems, with an iteration cost similar to stochastic gradient descent. Many machine learning and signal processing problems are traditionally cast as convex optimization problems. A common difficulty in solving these problems is the size of the data, where there are many observations ("large n") and each of these is large ("large p"). In this setting, online algorithms such as stochastic gradient descent which pass over the data only once, are usually preferred over batch algorithms, which require multiple passes ...

62L20 ; 68T05 ; 90C06 ; 90C25

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Research talks;Computer Science;Control Theory and Optimization;Probability and Statistics

Many machine learning and signal processing problems are traditionally cast as convex optimization problems. A common difficulty in solving these problems is the size of the data, where there are many observations ("large n") and each of these is large ("large p"). In this setting, online algorithms such as stochastic gradient descent which pass over the data only once, are usually preferred over batch algorithms, which require multiple passes over the data. Given n observations/iterations, the optimal convergence rates of these algorithms are $O(1/\sqrt{n})$ for general convex functions and reaches $O(1/n)$ for strongly-convex functions. In this tutorial, I will first present the classical results in stochastic approximation and relate them to classical optimization and statistics results. I will then show how the smoothness of loss functions may be used to design novel algorithms with improved behavior, both in theory and practice: in the ideal infinite-data setting, an efficient novel Newton-based stochastic approximation algorithm leads to a convergence rate of $O(1/n)$ without strong convexity assumptions, while in the practical finite-data setting, an appropriate combination of batch and online algorithms leads to unexpected behaviors, such as a linear convergence rate for strongly convex problems, with an iteration cost similar to stochastic gradient descent. Many machine learning and signal processing problems are traditionally cast as convex optimization problems. A common difficulty in solving these problems is the size of the data, where there are many observations ("large n") and each of these is large ("large p"). In this setting, online algorithms such as stochastic gradient descent which pass over the data only once, are usually preferred over batch algorithms, which require multiple passes ...

62L20 ; 68T05 ; 90C06 ; 90C25

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Estudos e comunicacoes do im , 0023

Localisation : Salle de manutention

algorithmique # programmation mathemathique

90C25

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- 74 p.
ISBN 978-0-89871-013-7

CBMS-NSF regional conference series in applied mathematics , 0016

Localisation : Collection 1er étage

Lagrangien # calcul de conjugué et de sous-gradient # dualité conjuguée # dérivée de fonction convexe # fonction convexe conjuguée en espace appareillé # fonctionnelle intégrale # optimisation convexe

26B25 ; 52A41 ; 90C25

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- 451 p.
ISBN 978-0-691-08069-7

Princeton mathematical series , 0028

Localisation : Ouvrage RdC (ROCK)

algèbre convexe # analyse convexe # correspondance de dualité # dérivée directionnelle et sous- gradient # fonction selle # problème d'extrenum contraint # représentation et inégalité # théorie du minimax

26A51 ; 90C25

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- 339 p.
ISBN 978-0-7923-4818-4

Nonconvex optimization and its applications , 0022

Localisation : Ouvrage RdC (TUY)

analyse convexe # fonction convexe # géométrie discrète # optimisation # programmation convexe # programmation mathématique # technique variationnelle

26B25 ; 52A41 ; 65K05 ; 90-02 ; 90C25

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- 418 p.
ISBN 978-3-540-56850-6

Grundlehren der mathematischen wissenschaften , 0305

Localisation : Collection 1er étage

fonction réelle # convexité # optimisation convexe # ensemble convexe # algorithme # analyse convexe # calcul de variation # fonction sous-linéaire # sous-différentiel # fonction lagrangienne # théorie de la dualité # gradient conjugué

26B25 ; 49-01 ; 90-01 ; 49J52 ; 90C25 ; 52A41 ; 65K10

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- xvi; 468 p.
ISBN 978-1-4419-9466-0

CMS books in mathematics

Localisation : Ouvrage RdC (BAUS)

analyse mathématique # espace de Hilbert # opérateur monotone # convexité # fonction convexe # programmation convexe # programmation non-linéaire # opérateur monotone # optimisation # approximation abstraite

41A50 ; 46-01 ; 46-02 ; 46Cxx ; 46C05 ; 47-01 ; 47-02 ; 47H05 ; 47H09 ; 47H10 ; 90-01 ; 90-02 ; 26A51 ; 26B25 ; 46N10 ; 47H04 ; 47N10 ; 52A05 ; 52A41 ; 65K05 ; 65K10 ; 90C25 ; 90C30

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- 310 p.
ISBN 978-0-387-29570-1

CMS Books in mathematics

Localisation : Ouvrage RdC (BORW)

fonction convexe # optimisation # théorie non-linéaire # calcul de variations # point intérieur # programmation convexe # dualité de Frenckel # théorème de Karush-Kuhn-Tucker # point fixe # théorème de Radenacker

90-01 ; 49-01 ; 90C51 ; 90C25 ; 49J53 ; 52A41 ; 46N10 ; 47H10

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- viii; 185 p.
ISBN 978-3-11-036103-2

Series in nonlinear analysis and applications , 0022

Localisation : Ouvrage RdC (BACA)

espace de Hadamard # convexité # géodésique # matrice d'Hadamard # G-espace # espace métrique # optimisation convexe

47H20 ; 49M20 ; 49M25 ; 49M27 ; 51F99 ; 52A01 ; 60B99 ; 60J10 ; 92D15 ; 90-02 ; 90C48 ; 90C25 ; 90C30

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- 293 p.

Grundlehren der mathematischen wissenschaften in einzeldarstellungen , 0163

Localisation : Collection 1er étage

convexité # dualité # ensemble convexe # fonction convexe # optimisation # théorème du point de selle

49-01 ; 49M45 ; 49N05 ; 49N15 ; 90C25

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- 230 p.
ISBN 978-2-7298-9751-2

Mathématiques pour le 2ème cycle

Localisation : Enseignement RdC (AZE)

analyse convexe # analyse fonctionnelle # analyse variationnelle # conjugaison # espace de Sobolev # fonction convexe # fonction lipschitzienne # optimisation convexe # problème aux limites # sous-différentiel # trace

46-01 ; 46E35 ; 49-01 ; 90-01 ; 90C25

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- 146 p.
ISBN 978-2-86601-320-2

Collection informatique

Localisation : Ouvrage RdC (TERR)

algorithmique # mathématique économique # modélisation # optimisation # optimisation sans contrainte # problème faiblement non linéaire # programmation linéaire # programmation mathématique

90A05 ; 90Bxx ; 90C25 ; 90C90 ; 90Cxx

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- xx; 347 p.
ISBN 978-1-4822-2207-4

Localisation : Ouvrage RdC (ALME)

théorie du point fixe # optimisation mathématique # analyse variationnelle # analyse convexe # analyse quasi-convexe # inégalités variationnelles # optimisation vectorielle # optimisation combinatoire multiobjectif

90-06 ; 90C25 ; 90C29 ; 90C27 ; 49J40 ; 49J53 ; 54C60 ; 54H25 ; 47H10 ; 26B25 ; 00B15

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- xviii; 439 p.
ISBN 978-0-387-34431-7

Graduate texts in mathematics , 0258

Localisation : Collection 1er étage

programmation mathématique # optimisation mathématique

90-01 ; 90C30 ; 90C46 ; 90C25 ; 90C05 ; 90C20 ; 90C34 ; 90C47 ; 49M37 ; 49N15 ; 49J53 ; 49J50 ; 49M15 ; 49K35 ; 65K05 ; 65K10 ; 52A05 ; 52A07 ; 52A35 ; 52A41 ; 52A40 ; 52A37 ; 90Cxx

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