Quadratically convergent algorithms and one-dimensional search schemes

DOI10.1007/BF00935882zbMath0238.65033MaRDI QIDQ2553146

Yanyan Li

Publication date: 1973

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05)

Related Items (18)

An efficient interfacing of golden-section and quadratic searches ⋮ A new class of supermemory gradient methods ⋮ A gradient-related algorithm with inexact line searches ⋮ A generalized direct search acceptable-point technique for use with descent-type multivariate algorithms ⋮ Convergence of supermemory gradient method ⋮ Efficient unidimensional direct search schemes for optimization problems. ⋮ Memory gradient method with Goldstein line search ⋮ A method to accelerate the rate of convergence of a class of optimization algorithms ⋮ A new descent algorithm with curve search rule ⋮ A class of quadratically convergent algorithms for constrained function minimization ⋮ Variable metric methods in Hilbert space with applications to control problems ⋮ On memory gradient method with trust region for unconstrained optimization ⋮ A new class of memory gradient methods with inexact line searches ⋮ The Topkis-Veinott algorithm for solving nonlinear programs with lower and upper bounded variables ⋮ Method of dual matrices for function minimization ⋮ Numerical experiments on dual matrix algorithms for function minimization ⋮ Convergence of descent method without line search ⋮ A new super-memory gradient method with curve search rule

Cites Work

• Unnamed Item
• Study on a supermemory gradient method for the minimization of functions
• Unified approach to quadratically convergent algorithms for function minimization
• Numerical experiments on quadratically convergent algorithms for function minimization
• Variable Metric Method for Minimization
• A Rapidly Convergent Descent Method for Minimization
• Function minimization by conjugate gradients
• Quasi-Newton Methods and their Application to Function Minimisation
• Computational experience with quadratically convergent minimisation methods
• Methods of conjugate gradients for solving linear systems

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