On Mixed Integer Nonsmooth Optimization

Eronen, Ville-Pekka; Westerlund, Tapio; Mäkelä, Marko M.

doi:10.1007/978-3-030-34910-3_16

Cited by 2 publications

(1 citation statement)

References 49 publications

(106 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These problems may be simple bound problems, problems with more specific constraints, or even problems with different types of variables. For instance, problems can be nonlinear, nonconvex, with equality (or inequality) constraints and with integer, continuous, or mixed integer variables [1,2,6].…”

Section: An Improved Multi-threaded Implementation Of the Mcsfilter O...mentioning

confidence: 99%

An Improved Multi-Threaded Implementation of the MCSFilter Optimization Algorithm

Monteiro

Rufino

Romanenko³

et al. 2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

OL2A 2022 provided a space for the research community on optimization and learning to get together and share the latest developments, trends, and techniques, as well as to develop new paths and collaborations. The conference had more than three hundred participants in an online and face-to-face environment throughout two days, discussing topics associated with optimization and learning, such as state-of-the-art applications related to multi-objective optimization, optimization for machine learning, robotics, health informatics, data analysis, optimization and learning under uncertainty, and Industry 4.0.Five special sessions were organized under the following topics: Trends in Engineering Education, Optimization in Control Systems Design, Measurements with the Internet of Things, Advances and Optimization in Cyber-Physical Systems, and Computer Vision Based on Learning Algorithms. The OL2A 2022 program included presentations of 56 accepted papers. All papers were carefully reviewed and selected from 145 submissions in an single-blind process. All the reviews were carefully carried out by a scientific committee of 102 qualified researchers from 21 countries, with each submission receiving at least 3 reviews.We would like to thank everyone who helped to make OL2A 2022 a success and hope that you enjoy reading this volume.

show abstract

Section: An Improved Multi-threaded Implementation Of the Mcsfilter O...mentioning

confidence: 99%

An Improved Multi-Threaded Implementation of the MCSFilter Optimization Algorithm

Monteiro

Rufino

Romanenko³

et al. 2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

Outer Approximation for Mixed-Integer Nonlinear Robust Optimization

Martina

Liers

Stingl

2022

J Optim Theory Appl

View full text Add to dashboard Cite

Currently, few approaches are available for mixed-integer nonlinear robust optimization. Those that do exist typically either require restrictive assumptions on the problem structure or do not guarantee robust protection. In this work, we develop an algorithm for convex mixed-integer nonlinear robust optimization problems where a key feature is that the method does not rely on a specific structure of the inner worst-case (adversarial) problem and allows the latter to be non-convex. A major challenge of such a general nonlinear setting is ensuring robust protection, as this calls for a global solution of the non-convex adversarial problem. Our method is able to achieve this up to a tolerance, by requiring worst-case evaluations only up to a certain precision. For example, the necessary assumptions can be met by approximating a non-convex adversarial via piecewise relaxations and solving the resulting problem up to any requested error as a mixed-integer linear problem.In our approach, we model a robust optimization problem as a nonsmooth mixed-integer nonlinear problem and tackle it by an outer approximation method that requires only inexact function values and subgradients. To deal with the arising nonlinear subproblems, we render an adaptive bundle method applicable to this setting and extend it to generate cutting planes, which are valid up to a known precision. Relying on its convergence to approximate critical points, we prove, as a consequence, finite convergence of the outer approximation algorithm.As an application, we study the gas transport problem under uncertainties in demand and physical parameters on realistic instances and provide computational results demonstrating the efficiency of our method.

show abstract

On Mixed Integer Nonsmooth Optimization

Cited by 2 publications

References 49 publications

An Improved Multi-Threaded Implementation of the MCSFilter Optimization Algorithm

An Improved Multi-Threaded Implementation of the MCSFilter Optimization Algorithm

Outer Approximation for Mixed-Integer Nonlinear Robust Optimization

Contact Info

Product

Resources

About