CONORBIT: constrained optimization by radial basis function interpolation in trust regions

DOI10.1080/10556788.2016.1226305zbMath1367.65091OpenAlexW2526305524MaRDI QIDQ5268937

Rommel G. Regis, Stefan M. Wild

Publication date: 21 June 2017

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://www.osti.gov/biblio/1373699

zbMATH Keywords

constrained optimizationsimulation-based optimizationnumerical resulttrust regionradial basis function interpolationblack-box optimizationderivative-free algorithmcomputationally expensive functionfully linear model

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Interior-point methods (90C51) Methods of reduced gradient type (90C52)

Related Items (4)

Accelerated random search for constrained global optimization assisted by radial basis function surrogates ⋮ Efficient global optimization for high-dimensional constrained problems by using the Kriging models combined with the partial least squares method ⋮ CONORBIT ⋮ Derivative-free optimization methods

Uses Software

• NEWUOA
• NOMAD
• BOBYQA
• COBYLA2
• UOBYQA
• NOWPAC
• NLopt
• OPTI
• Boosters

Cites Work

• Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions
• Convex analysis and nonlinear optimization. Theory and examples.
• Nonsmooth optimization through mesh adaptive direct search and variable neighborhood search
• Recent progress in unconstrained nonlinear optimization without derivatives
• UOBYQA: unconstrained optimization by quadratic approximation
• Constrained optimization involving expensive function evaluations: A sequential approach
• Optimizing radial basis functions by d.c. programming and its use in direct search for global derivative-free optimization
• A quasi-multistart framework for global optimization of expensive functions using response surface models
• Geometry of interpolation sets in derivative free optimization
• A Linesearch-Based Derivative-Free Approach for Nonsmooth Constrained Optimization
• Algorithm 909
• Sequential Penalty Derivative-Free Methods for Nonlinear Constrained Optimization
• Global Convergence of Radial Basis Function Trust Region Derivative-Free Algorithms
• On the Convergence of Pattern Search Algorithms
• A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bounds
• Using Sampling and Simplex Derivatives in Pattern Search Methods
• Introduction to Derivative-Free Optimization
• ORBIT: Optimization by Radial Basis Function Interpolation in Trust-Regions
• Use of quadratic models with mesh-adaptive direct search for constrained black box optimization
• Benchmarking Derivative-Free Optimization Algorithms
• A Progressive Barrier for Derivative-Free Nonlinear Programming
• Mesh Adaptive Direct Search Algorithms for Constrained Optimization
• A Simplex Method for Function Minimization
• Benchmarking optimization software with performance profiles.
• Nonlinear programming without a penalty function.

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