Self-Scaling Variable Metric Algorithms without Line Search for Unconstrained Minimization
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Publication:4060281
DOI10.2307/2005523zbMath0304.65045OpenAlexW4251660957MaRDI QIDQ4060281
Publication date: 1973
Full work available at URL: https://doi.org/10.2307/2005523
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Cites Work
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- Unified approach to quadratically convergent algorithms for function minimization
- Variable metric algorithms: Necessary and sufficient conditions for identical behaviour of nonquadratic functions
- Self-Scaling Variable Metric (SSVM) Algorithms
- Self-Scaling Variable Metric (SSVM) Algorithms
- A Rapidly Convergent Descent Method for Minimization
- Quasi-Newton Methods and their Application to Function Minimisation
- Variance algorithm for minimization
- On a Numerical Instability of Davidon-Like Methods
- Variations on Variable-Metric Methods
- The Convergence of a Class of Double-rank Minimization Algorithms
- A new approach to variable metric algorithms
- On Steepest Descent
- Recent advances in unconstrained optimization
- Quasi-Newton Methods for Unconstrained Optimization