Algorithm 1053: SOLNP+: A Derivative-Free Solver for Constrained Nonlinear Optimization

SOLNP \(+\) is a derivative-free solver for constrained nonlinear optimization. It starts from SOLve Nonlinear Programming (SOLNP) proposed in 1989 by Ye. The main ideas are to use finite difference to approximate the gradient of the objective function and constraints, and use augmented Lagrangian method and sequential quadratic programming to deal with nonlinear constraints. We incorporate the techniques of implicit filtering, a new restart mechanism, and a modern quadratic programming solver into this new version with an ANSI C implementation. The algorithm exhibits a great advantage in running time and robustness under noise compared with the old version implemented in MATLAB. The numerical experiments show that SOLNP \(+\) is comparable with two widely used solvers, COBYLA and NOMAD. SOLNP \(+\) is available at https://github.com/COPT-Public/SOLNP_plus .

Authors

Institutions

Publication Details

Journal
ACM Transactions on Mathematical Software
Published
2024-10-10
DOI
https://doi.org/10.1145/3699956
Citations
1
Primary Topic
Advanced Optimization Algorithms Research
Type
article
Field-Weighted Citation Impact
0.73
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Algorithm 1053: SOLNP+: A Derivative-Free Solver for Constrained Nonlinear Optimization

Dongdong Ge, Tianhao Liu, Yinyu Ye, Jiyuan Tan et al.
1 citations
ACM Transactions on Mathematical Software
Advanced Optimization Algorithms Research
0.73
article

Algorithm 1053: SOLNP+: A Derivative-Free Solver for Constrained Nonlinear Optimization

Dongdong Ge, Tianhao Liu, Yinyu Ye, Jiyuan Tan, Jinsong Liu
article en
1 citations

Abstract

SOLNP \(+\) is a derivative-free solver for constrained nonlinear optimization. It starts from SOLve Nonlinear Programming (SOLNP) proposed in 1989 by Ye. The main ideas are to use finite difference to approximate the gradient of the objective function and constraints, and use augmented Lagrangian method and sequential quadratic programming to deal with nonlinear constraints. We incorporate the techniques of implicit filtering, a new restart mechanism, and a modern quadratic programming solver into this new version with an ANSI C implementation. The algorithm exhibits a great advantage in running time and robustness under noise compared with the old version implemented in MATLAB. The numerical experiments show that SOLNP \(+\) is comparable with two widely used solvers, COBYLA and NOMAD. SOLNP \(+\) is available at https://github.com/COPT-Public/SOLNP_plus .

ACM Transactions on Mathematical SoftwareVol. 50(4)
Shanghai University of Finance and Economics (CN), Shanghai Jiao Tong University (CN), Stanford University (US)
Openalex Percentile: Top 30%
Advanced Optimization Algorithms Research
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.