6. Worst-case Complexity in Continuous Multi-Objective Optimization
Invited abstract in session TC-10: Continuous Multi-Objective Optimization: Algorithms and Complexity Analyses, stream Multiobjective and Vector Optimization.
Tuesday, 14:00-16:00Room: B100/8011
Authors (first author is the speaker)
| 1. | Rohollah Garmanjani
|
| NOVA Math, Universidade NOVA de Lisboa |
Abstract
In this talk, we delve into the worst-case complexity of continuous optimization, which quantifies the computational effort required for an algorithm to reduce a stationarity measure below a given positive threshold in the worst-case scenario. We begin by providing an overview of worst-case complexity in single-objective optimization, outlining foundational results to serve as a benchmark.
We then shift our focus to the more complex realm of multiobjective optimization, highlighting its distinct challenges and recent advancements. Lastly, we examine the worst-case complexity of a trust-region algorithm, analyzing its performance under both convexity and strong convexity assumptions.
Keywords
- Multi-objective optimization
Status: accepted
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