The estimation of the hessian matrix in nonlinear least squares problems with non-zero residuals
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Publication:4140302
DOI10.1007/BF01593770zbMath0365.65043MaRDI QIDQ4140302
Publication date: 1977
Published in: Mathematical Programming (Search for Journal in Brave)
Related Items (14)
Numerical experiments with variations of the Gauss-Newton algorithm for nonlinear least squares ⋮ Convergence theory for the structured BFGS secant method with an application to nonlinear least squares ⋮ Local analysis of a spectral correction for the Gauss-Newton model applied to quadratic residual problems ⋮ Hybrid methods for large sparse nonlinear least squares ⋮ Structured two-point stepsize gradient methods for nonlinear least squares ⋮ A structured diagonal Hessian approximation method with evaluation complexity analysis for nonlinear least squares ⋮ Superlinearly convergent exact penalty methods with projected structured secant updates for constrained nonlinear least squares ⋮ Factorized quasi-Newton methods for nonlinear least squares problems ⋮ A two-dimensional search used with a nonlinear least-squares solver ⋮ Stable factorized quasi-Newton methods for nonlinear least-squares problems ⋮ A brief survey of methods for solving nonlinear least-squares problems ⋮ Structured symmetric rank-one method for unconstrained optimization ⋮ A two-step superlinearly convergent projected structured BFGS method for constrained nonlinear least squares ⋮ Variable metric methods for unconstrained optimization and nonlinear least squares
Uses Software
Cites Work
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- A Note on Minimization Algorithms which make Use of Non-quardratic Properties of the Objective Function
- Specialised versus general-purpose algorithms for minimising functions that are sums of squared terms
- A minimal point of a finite metric set
- A New Algorithm for Unconstrained Optimization
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