A comparison between fixed-basis and variable-basis schemes for function approximation and functional optimization
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Publication:411092
DOI10.1155/2012/806945zbMath1242.49064OpenAlexW2047984666WikidataQ58907386 ScholiaQ58907386MaRDI QIDQ411092
Publication date: 4 April 2012
Published in: Journal of Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1155/2012/806945
functional optimizationfunction approximationfixed-basis and variable-basis approximation schemesinfinite programming
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Cites Work
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- Can dictionary-based computational models outperform the best linear ones?
- Some comparisons of complexity in dictionary-based and linear computational models
- Suboptimal solutions to dynamic optimization problems via approximations of the policy functions
- Accuracy of suboptimal solutions to kernel principal component analysis
- Approximation schemes for functional optimization problems
- On uniformly convex functionals
- Uniform approximation by neural networks
- Perturbations, approximations and sensitivity analysis of optimal control systems
- Large-eddy simulation of high-Reynolds-number supersonic boundary layers using the approximate deconvolution model and a rescaling and recycling technique
- The extended Ritz method for functional optimization: overview and applications to single-person and team optimal decision problems
- Universal approximation bounds for superpositions of a sigmoidal function
- Comparison of worst case errors in linear and neural network approximation
- Sequential greedy approximation for certain convex optimization problems
- 10.1162/153244303322753616
- Sup-norm approximation bounds for networks through probabilistic methods
- Error Estimates for Approximate Optimization by the Extended Ritz Method
- Approximation by superpositions of a sigmoidal function
- Approximating networks and extended Ritz method for the solution of functional optimization problems
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