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Sequential Convex Programming Methods for Solving Nonlinear Optimization Problems with DC constraints

Year: 2011

Type: preprint

Abstract: This paper investigates the relation between sequential convex programming (SCP) as, e.g., defined in [24] and DC (difference of two convex functions) programming. We first present an SCP algorithm for solving nonlinear optimization problems with DC constraints and prove its convergence. Then we combine the proposed algorithm with a relaxation technique to handle inconsistent linearizations. Numerical tests are performed to investigate the behaviour of the class of algorithms.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1107.5841

Authors: Quoc, Tran Dinh, Diehl, Moritz

Institution: KU Leuven

Language: English

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: Helmholtz-Gemeinschaft, Belgian Federal Science Policy Office, Fonds Wetenschappelijk Onderzoek, Vlaamse regering, Eurostars

Awards: G1747445222, G2425542961, G3007518541, G3145199608, G3287131444, G3795546119, Moving horizon estimation: robust stability, robust performance and interaction with model predictive control., G5744732752, G5899396625, G7294235563, G7561699830, G7993048059, G8269176613, G891549126, G991180673

OpenAlex ID: W1671720504

Cites: 20

Cited by: 49

Related to: 10