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An incremental least squares algorithm for large scale linear classification
Cassioli, Andrea; Chiavaioli, A.; Manes, Costanzo; … - In: European journal of operational research : EJOR 224 (2013) 3, pp. 560-565
Persistent link: https://www.econbiz.de/10009683761
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Improvement of Kriging interpolation with learning kernel in environmental variables study
Xu, Te; Liu, Yongxia; Tang, Lixin; Liu, Chang - In: International journal of production research 60 (2022) 4, pp. 1284-1297
Persistent link: https://www.econbiz.de/10013170846
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An Adaptive Nonlinear Least Square Algorithm
Dennis, John E.; Gay, David M.; Welsch, Roy E. - 2021
NL2SOL is a modular program for solving the nonlinear least-squares problem that incorporates a number of novel features. It maintains a secant approximation S to the second-order part of the least-squares Hessian and adaptively decides when to use this approximation. We have found it very...
Persistent link: https://www.econbiz.de/10013219332
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An Adaptive Nonlinear Least Square Algorithm
Dennis, John E. - 1977
NL2SOL is a modular program for solving the nonlinear least-squares problem that incorporates a number of novel features. It maintains a secant approximation S to the second-order part of the least-squares Hessian and adaptively decides when to use this approximation. We have found it very...
Persistent link: https://www.econbiz.de/10012478936
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A more efficient algorithm for Convex Nonparametric Least Squares
Lee, Chia-yen; Johnson, Andrew L.; Moreno-Centeno, Erick; … - In: European journal of operational research : EJOR 227 (2013) 2, pp. 391-400
Persistent link: https://www.econbiz.de/10009725603
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A More Efficient Algorithm for Convex Nonparametric Least Squares
Lee, Chia-Yen - 2013
significant even for relatively small samples. This study proposes a generic algorithm that improves the computational performance … evaluate the performance of six variants of the proposed algorithm. These experimental results indicate that a particular … variant is most efficient given the sample size and the dimensionality. The computational benefits of the new algorithm are …
Persistent link: https://www.econbiz.de/10013091856
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Least Squares Fitting of Chacón-Gielis Curves By the Particle Swarm Method of Optimization
Mishra, Sudhanshu K. - 2007
Particle Swarm optimization algorithm has been used. It has been found that although the curve-fitting exercise may be …
Persistent link: https://www.econbiz.de/10012731627
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Recursive importance sketching for rank constrained least squares : algorithms and high-order convergence
Luo, Yuetian; Huang, Wen; Li, Xudong; Zhang, Anru - In: Operations research 72 (2024) 1, pp. 237-256
Persistent link: https://www.econbiz.de/10014505097
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A semi-closed form approximation of arbitrage‑free call option price surface
Kundu, Arindam; Kumar, Sumit; Tomar, Nutan Kumar - In: Computational economics 63 (2024) 4, pp. 1431-1457
Persistent link: https://www.econbiz.de/10014549032
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Least Squares Monte Carlo and Approximate Linear Programming : Error Bounds and Energy Real Option Application
Nadarajah, Selvaprabu - 2018
Least squares Monte Carlo (LSM) is an approximate dynamic programming (ADP) technique commonly used for the valuation of high dimensional financial and real options, but has broader applicability. It is known that the regress-later version of this method is an approximate linear programming...
Persistent link: https://www.econbiz.de/10012912912