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Multiparametric curve fitting XIV. Modus operandi of the least-squares algorithm MINOPT
1Department of Textile Materials, Technical University, CS-461 17 Liberec, Czech Republic.
Talanta
|February 1, 1993
Summary
The MINOPT algorithm offers efficient nonlinear regression by combining Gauss-Newton and gradient methods. It provides accurate parameter estimates, outperforming other commercial regression packages.
Area of Science:
- Computational chemistry
- Statistical modeling
- Numerical analysis
Background:
- Nonlinear regression is crucial for analyzing complex datasets.
- Existing algorithms have limitations in convergence speed and accuracy.
- The CHEMSTAT package provides tools for statistical analysis.
Purpose of the Study:
- Introduce the Hybrid Least-Squares Algorithm MINOPT.
- Evaluate MINOPT's performance in nonlinear regression.
- Compare MINOPT with existing commercial regression methods.
Main Methods:
- Developed the MINOPT algorithm, a hybrid approach.
- Applied MINOPT to six selected nonlinear regression models.
- Compared minimization quality and parameter estimate accuracy.
Main Results:
- MINOPT combines rapid convergence near the minimum (Gauss-Newton) with robust convergence far from it (gradient methods).
- Evaluated the quality of minimization and accuracy of parameter estimates.
- Compared MINOPT's performance against five commercial regression packages.
Conclusions:
- MINOPT demonstrates effective nonlinear regression capabilities.
- The hybrid approach offers advantages in both speed and accuracy.
- MINOPT shows competitive or superior performance compared to commercial alternatives.
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