This paper introduces three (one linear and two nonlinear) automatic scaling techniques for NLPs with states and constraints spread over several orders of magnitude, without requiring complex off-the-shelf external tools. All of these methods have been compared to standard techniques and applied to three problems using SNOPT and IPOPT. The results confirm that the proposed techniques significantly improve the NLP conditioning, yielding more reliable and in some cases, faster NLP solutions. ©2014 Elsevier B.V. All rights reserved.
Sagliano, M. (2014). Performance analysis of linear and nonlinear techniques for automatic scaling of discretized control problems. OPERATIONS RESEARCH LETTERS, 42(3), 213-216 [10.1016/j.orl.2014.03.003].
Performance analysis of linear and nonlinear techniques for automatic scaling of discretized control problems
Sagliano M.
2014
Abstract
This paper introduces three (one linear and two nonlinear) automatic scaling techniques for NLPs with states and constraints spread over several orders of magnitude, without requiring complex off-the-shelf external tools. All of these methods have been compared to standard techniques and applied to three problems using SNOPT and IPOPT. The results confirm that the proposed techniques significantly improve the NLP conditioning, yielding more reliable and in some cases, faster NLP solutions. ©2014 Elsevier B.V. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
