Diminishing stepsize methods for nonconvex composite problems via ghost penalties: from the general to the convex regular constrained case-Reference-Cited by-同舟云学术

Diminishing stepsize methods for nonconvex composite problems via ghost penalties: from the general to the convex regular constrained case

Published:2020-12-02 Issue: Volume: Page:1-27
ISSN:1055-6788
Container-title:Optimization Methods and Software
language:en
Short-container-title:Optimization Methods and Software

Author:

Facchinei Francisco¹,Kungurtsev Vyacheskav²,Lampariello Lorenzo³,Scutari Gesualdo⁴

Affiliation:

1. Department of Computer, Control, and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy

2. Faculty of Electrical Engineering, Department of Computer Science, Czech Technical University in Prague, Prague, Czech Republic

3. Roma Tre University, Rome, Italy

4. Purdue University, School of Industrial Engineering, West-Lafayette, IN, USA

Funder

Progetto di Ateneo Distributed optimization algorithms for Big Data

OP VVV

MIUR

NSF

DOE

ARO

Publisher

Informa UK Limited

Subject

Applied Mathematics,Control and Optimization,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/10556788.2020.1854253

Reference15 articles.

1. Non-Linear Parametric Optimization

2. A sequential quadratic programming method for potentially infeasible mathematical programs

3. Mathematical Programming and Control Theory

4. F. Facchinei, V. Kungurtsev, L. Lampariello, and G. Scutari, Iteration complexity of a fixed-stepsize sqp method for nonconvex optimization with convex constraints, in Numerical Analysis and Optimization, NAO-V, Muscat, Oman, January 2020, Springer Proceedings in Mathematics & Statistics, M. Al-Baali, L. Grandinetti, and A. Purnama, eds, Springer, to appear.

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