Logarithmic-Barrier Decomposition Interior-Point Methods for Stochastic Linear Optimization in a Hilbert Space
Author:
Affiliation:
1. Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA;
2. Department of Mathematics, The University of Jordan, Amman, Jordan
Publisher
Informa UK Limited
Subject
Control and Optimization,Computer Science Applications,Signal Processing,Analysis
Link
https://www.tandfonline.com/doi/pdf/10.1080/01630563.2019.1709499
Reference27 articles.
1. A Flexible Framework for Cubic Regularization Algorithms for Nonconvex Optimization in Function Space
2. Kuhn–Tucker Conditions for a Convex Programming Problem in Banach Spaces Partially Ordered by Cone with Empty Interior
3. A Parametric Newton Method for Optimization Problems in Hilbert Spaces
4. Necessary and Sufficient Conditions for Qualitative Properties of Infinite Dimensional Linear Programming Problems
5. A Proximal-Type Method for Convex Vector Optimization Problem in Banach Spaces
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