CGRS -- an advanced hybrid method for global optimization of continuous functions closely coupling extended random search and conjugate gradient method
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Publication:679580
DOI10.1016/j.cam.2017.10.018zbMath1390.90442OpenAlexW2765173154MaRDI QIDQ679580
Christian Gnandt, Rainer Callies
Publication date: 11 January 2018
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cam.2017.10.018
global optimizationconjugate gradient methodhybrid approachrandom searchconvergence in probabilitydistribution-based region control
Nonconvex programming, global optimization (90C26) Approximation methods and heuristics in mathematical programming (90C59)
Uses Software
Cites Work
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- Optimization by Simulated Annealing
- A class of globally convergent conjugate gradient methods
- Convergence guarantees for generalized adaptive stochastic search methods for continuous global optimization
- A review of recent advances in global optimization
- Conjugate gradient algorithms in nonconvex optimization
- A collection of test problems for constrained global optimization algorithms
- Optimization. Algorithms and consistent approximations
- On trust region methods for unconstrained minimization without derivatives
- A hybrid algorithm for identifying global and local minima when optimizing functions with many minima.
- Handbook of global optimization. Vol. 2
- Evolution strategies. A comprehensive introduction
- A hybrid descent method for global optimization
- A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum
- DESA: a new hybrid global optimization method and its application to analog integrated circuit sizing
- Improving hit-and-run for global optimization
- An efficient algorithm for large scale global optimization of continuous functions
- Derivative-free optimization: a review of algorithms and comparison of software implementations
- A particle swarm pattern search method for bound constrained global optimization
- A combined global \& local search (CGLS) approach to global optimization
- A new family of conjugate gradient methods
- Trust-Region Methods Without Using Derivatives: Worst Case Complexity and the NonSmooth Case
- A Hybrid Evolutionary Algorithm for Global Optimization
- On the Convergence of Pattern Search Algorithms
- Evaluating Derivatives
- Spectral conjugate gradient methods with sufficient descent property for large-scale unconstrained optimization
- Algorithm 851
- Introduction to Derivative-Free Optimization
- Computing Forward-Difference Intervals for Numerical Optimization
- Stochastic global optimization methods part II: Multi level methods
- Adaptive Numerical Differentiation
- Numerical Differentiation of Analytic Functions
- Minimization by Random Search Techniques
- Note on the Convergence of Simulated Annealing Algorithms
- On the Convergence and Applications of Generalized Simulated Annealing
- Hybrid simulated annealing and direct search method for nonlinear unconstrained global optimization
- Introduction to Stochastic Search and Optimization
- The Theory and Practice of Simulated Annealing
- A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property
- Global Convergence of General Derivative-Free Trust-Region Algorithms to First- and Second-Order Critical Points
- A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search
- Function minimization by conjugate gradients
- The Limited Memory Conjugate Gradient Method
- Global convergence of the DY conjugate gradient method with Armijo line search for unconstrained optimization problems
- A New Method of Constrained Optimization and a Comparison With Other Methods
- Convergence Conditions for Ascent Methods
- Methods of conjugate gradients for solving linear systems
- Global optimization based on local searches