Convergence in distribution of randomized algorithms: the case of partially separable optimization
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
1 Online-Ressource.
- Sprache
-
Englisch
- Erschienen in
-
Convergence in distribution of randomized algorithms: the case of partially separable optimization ; day:27 ; month:7 ; year:2024 ; pages:1-36
Mathematical programming ; (27.7.2024), 1-36
- Urheber
-
Luke, D. Russell
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s10107-024-02124-w
- URN
-
urn:nbn:de:101:1-2410122127342.731041029701
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:38 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Luke, D. Russell
- SpringerLink (Online service)
Ähnliche Objekte (12)
Learning algorithms for separable approximations of stochastic optimization problems
Convergence of gradient-like dynamical systems and optimization algorithms
Global convergence of multidirectional algorithms for unconstrained optimization in normed spaces
Convergence properties of evolutionary algorithms
Algorithms for matching partially labelled sequence graphs
Vector and matrix apportionment problems and separable convex integer optimization
Vector and matrix apportionment problems and separable convex integer optimization
CONVERGENCE WITH RESPECT TO THE IDEAL OF MEAGER SETS IN SEPARABLE CATEGORY BASES
Bayesian optimization with partially specified queries
Derivative-free separable quadratic modeling and cubic regularization for unconstrained optimization
On convergence of iterative thresholding algorithms to approximate sparse solution for composite nonconvex optimization
Algorithms for separable nonlinear least squares with application to modelling time-resolved spectra
Learning algorithms for separable approximations of stochastic optimization problems
Convergence of gradient-like dynamical systems and optimization algorithms
Global convergence of multidirectional algorithms for unconstrained optimization in normed spaces
Convergence properties of evolutionary algorithms
Algorithms for matching partially labelled sequence graphs
Vector and matrix apportionment problems and separable convex integer optimization
Vector and matrix apportionment problems and separable convex integer optimization
CONVERGENCE WITH RESPECT TO THE IDEAL OF MEAGER SETS IN SEPARABLE CATEGORY BASES
Bayesian optimization with partially specified queries
Derivative-free separable quadratic modeling and cubic regularization for unconstrained optimization
On convergence of iterative thresholding algorithms to approximate sparse solution for composite nonconvex optimization
Algorithms for separable nonlinear least squares with application to modelling time-resolved spectra
Learning algorithms for separable approximations of stochastic optimization problems
Convergence of gradient-like dynamical systems and optimization algorithms
Global convergence of multidirectional algorithms for unconstrained optimization in normed spaces
Convergence properties of evolutionary algorithms
Algorithms for matching partially labelled sequence graphs
Vector and matrix apportionment problems and separable convex integer optimization
Vector and matrix apportionment problems and separable convex integer optimization
CONVERGENCE WITH RESPECT TO THE IDEAL OF MEAGER SETS IN SEPARABLE CATEGORY BASES
Bayesian optimization with partially specified queries
Derivative-free separable quadratic modeling and cubic regularization for unconstrained optimization
On convergence of iterative thresholding algorithms to approximate sparse solution for composite nonconvex optimization