A derivative-based bracketing scheme for univariate minimization and the conjugate gradient method
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Publication:1825603
DOI10.1016/0898-1221(89)90177-6zbMath0684.65063OpenAlexW1996090111MaRDI QIDQ1825603
Publication date: 1989
Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/0898-1221(89)90177-6
numerical examplesquadratic convergencebisection methodHermite cubic interpolationbracketing strategyconjugate gradient search schemederivative-based univariate minimization algorithm
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Application of the dual active set algorithm to quadratic network optimization, Dual techniques for constrained optimization, A derivative-free bracketing scheme for univariate minimization, Analysis and implementation of a dual algorithm for constrained optimization
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Cites Work
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- A new three-term conjugate gradient method
- A derivative-free bracketing scheme for univariate minimization
- An efficient line search for nonlinear least squares
- A globally convergent method for nonlinear programming
- On the relation between quadratic termination and convergence properties of minimization algorithms. Part I. Theory
- On the relation between quadratic termination and convergence properties of minimization algorithms. Part II. Applications
- Rates of convergence of a one-dimensional search based on interpolating polynomials
- Convergence of the conjugate gradient method with computationally convenient modifications
- An approach to nonlinear programming
- Stationarity and superlinear convergence of an algorithm for univariate locally lipschitz constrained minimization
- Descent Property and Global Convergence of the Fletcher—Reeves Method with Inexact Line Search
- A Computational Comparison of the Ellipsoid Algorithm with Several Nonlinear Programming Algorithms
- Convergence conditions for restarted conjugate gradient methods with inaccurate line searches
- Design and Testing of a Generalized Reduced Gradient Code for Nonlinear Programming
- Algorithms for nonlinear constraints that use lagrangian functions
- Conjugate Gradient Methods with Inexact Searches
- The Conjugate Gradient Method for Linear and Nonlinear Operator Equations
- A Correction Concerning the Convergence Rate for the Conjugate Gradient Method
- Efficient Implementations of the Polak–Ribière Conjugate Gradient Algorithm
- Practical convergence conditions for unconstrained optimization
