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Bibliographic Details
Main Authors: Joshy, Anugrah Jo, Hwang, John T.
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2408.13420
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author Joshy, Anugrah Jo
Hwang, John T.
author_facet Joshy, Anugrah Jo
Hwang, John T.
contents PySLSQP is a seamless interface for using the SLSQP algorithm from Python. It wraps the original SLSQP Fortran code sourced from the SciPy repository and provides a host of new features to improve the research utility of the original algorithm. Some of the additional features offered by PySLSQP include auto-generation of unavailable derivatives using finite differences, independent scaling of the problem variables and functions, access to internal optimization data, live-visualization, saving optimization data from each iteration, warm/hot restarting of optimization, and various other utilities for post-processing.
format Preprint
id arxiv_https___arxiv_org_abs_2408_13420
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle PySLSQP: A transparent Python package for the SLSQP optimization algorithm modernized with utilities for visualization and post-processing
Joshy, Anugrah Jo
Hwang, John T.
Mathematical Software
Numerical Analysis
G.1.6; J.2
PySLSQP is a seamless interface for using the SLSQP algorithm from Python. It wraps the original SLSQP Fortran code sourced from the SciPy repository and provides a host of new features to improve the research utility of the original algorithm. Some of the additional features offered by PySLSQP include auto-generation of unavailable derivatives using finite differences, independent scaling of the problem variables and functions, access to internal optimization data, live-visualization, saving optimization data from each iteration, warm/hot restarting of optimization, and various other utilities for post-processing.
title PySLSQP: A transparent Python package for the SLSQP optimization algorithm modernized with utilities for visualization and post-processing
topic Mathematical Software
Numerical Analysis
G.1.6; J.2
url https://arxiv.org/abs/2408.13420