A corrector-predictor arc search interior-point algorithm for symmetric optimization

Pirhaji, Mohammad; Mansouri, Hossein

doi:10.1016/s0252-9602(18)30813-0

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2020

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“…Yang et al [22] presented a Mehrotra-type predictor-corrector infeasibleinterior-point method for symmetric optimization based on a new one-norm neighborhood. A corrector-predictor interiorpoint algorithm is proposed for symmetric optimization by Pirhaji et al [23]. Interior point method is a very appealing approach to the optimal power flow (OPF) problem mainly due to its speed of convergence and ease of handling inequality constraints.…”

Section: Introductionmentioning

confidence: 99%

An Improved Interior Point Algorithm for Quantile Regression

Zhao

2020

IEEE Access

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Quantile regression is a powerful statistical technique for estimating the quantiles of a conditional distribution on the values of covariates. It has been widely used in many fields. In this paper, an improved interior point algorithm for quantile regression is proposed. The algorithm introduces multiple centrality corrections technique into the interior point algorithm for quantile regression. The purpose of introducing the multiple centrality corrections technique is to reduce the overall solution time required to solve a quantile regression problem. The computational experiments results constitute evidence of the improvement obtained with the use of multiple centrality correction technique combined with the interior point algorithm. INDEX TERMS quantile regression, multiple centrality corrections, interior point algorithm.

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Section: Introductionmentioning

confidence: 99%

An Improved Interior Point Algorithm for Quantile Regression

Zhao

2020

IEEE Access

View full text Add to dashboard Cite

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A corrector-predictor arc search interior-point algorithm for symmetric optimization

Cited by 1 publication

References 20 publications

An Improved Interior Point Algorithm for Quantile Regression

An Improved Interior Point Algorithm for Quantile Regression

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