FrankWolfe.jl: a high-performance and flexible toolbox for Frank-Wolfe algorithms and conditional gradients
Year of publication: |
2022
|
---|---|
Authors: | Besançon, Mathieu ; Carderera, Alejandro ; Pokutta, Sebastian |
Published in: |
INFORMS journal on computing : JOC ; charting new directions in operations research and computer science ; a journal of the Institute for Operations Research and the Management Sciences. - Linthicum, Md. : INFORMS, ISSN 1526-5528, ZDB-ID 2004082-9. - Vol. 34.2022, 5, p. 2611-2620
|
Subject: | first-order methods | nonlinear programming | optimization software | Theorie | Theory | Mathematische Optimierung | Mathematical programming |
-
Online first-order framework for robust convex optimization
Ho-Nguyen, Nam, (2018)
-
Exact worst-case performance of first-order methods for composite convex optimization
Taylor, Adrien B., (2016)
-
PAMS.py : a GAMS-like modeling system based on Python and SAGE
Roson, Roberto, (2016)
- More ...
-
How many clues to give? : a bilevel formulation for the minimum Sudoku clue problem
Tjusila, Gennesaret, (2024)
-
Flexible differentiable optimization via model transformations
Besançon, Mathieu, (2024)
-
Managing liquidity : optimal degree of centralization
Pokutta, Sebastian, (2011)
- More ...