Convergence in distribution of randomized algorithms: the case of partially separable optimization
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
1 Online-Ressource.
- Language
-
Englisch
- Bibliographic citation
-
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
- Creator
-
Luke, D. Russell
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1007/s10107-024-02124-w
- URN
-
urn:nbn:de:101:1-2410122127342.731041029701
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:38 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Luke, D. Russell
- SpringerLink (Online service)
Other Objects (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