A New Descent Algorithm Using the Three-Step Discretization Method for Solving Unconstrained Optimization Problems

Torabi, Mina; Hosseini, M. M.

doi:10.3390/math6040063

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…The issue on search direction modification for SD method has grown importance in light of recent as in 2018, [7] introduced a new descent method that used a three-step discretization method which has an intermediate step between the initial point, x 0 to the next iterate point, x k+1 . In 2016, [8] proposed a search direction of the SD method that possessed global convergence properties.…”

Section: Evolution Of Steepest Descent Methodsmentioning

confidence: 99%

An Efficient Three-Term Iterative Method for Estimating Linear Approximation Models in Regression Analysis

et al. 2020

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This study employs exact line search iterative algorithms for solving large scale unconstrained optimization problems in which the direction is a three-term modification of iterative method with two different scaled parameters. The objective of this research is to identify the effectiveness of the new directions both theoretically and numerically. Sufficient descent property and global convergence analysis of the suggested methods are established. For numerical experiment purposes, the methods are compared with the previous well-known three-term iterative method and each method is evaluated over the same set of test problems with different initial points. Numerical results show that the performances of the proposed three-term methods are more efficient and superior to the existing method. These methods could also produce an approximate linear regression equation to solve the regression model. The findings of this study can help better understanding of the applicability of numerical algorithms that can be used in estimating the regression model.

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Section: Evolution Of Steepest Descent Methodsmentioning

confidence: 99%

An Efficient Three-Term Iterative Method for Estimating Linear Approximation Models in Regression Analysis

et al. 2020

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“…Based on that, they presented a starting point strategy in non-linear programming. An appropriate choice of an initial starting point reduces computational cost and improves the speed of convergence, see Torabi and Hosseini (2018).…”

Section: Introductionmentioning

confidence: 99%

A note on a Comparison of Starting Points for the Generation of D-optimal Second-order Designs

Umelo-Ibemere¹

2020

AJAS

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Agricultural monitoring has become an absolute necessity in the Sahel countries, especially with climate change which constitutes a real threat for this sector. The aim of this work is to develop a methodology for identifying crops and mapping agricultural areas using Sentinel-2 data from the Copernicus program. The purpose of this work consisted in discriminating the crops of millet, maize and peanuts. This is to analyse the scientific and technical obstacles related to this problem. For this, we have made a mathematical analysis of optical satellite images. High temporal and spatial resolution images (10m to 60m) of Sentinel 2 sensors were used in this work. This unique set of data coupled with field data, has permitted to carry out a diagnosis of land cover and cultivated land surfaces, and evaluating the contribution of this type of data for crop forecast

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Descent three-term DY-type conjugate gradient methods for constrained monotone equations with application

et al. 2021

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A New Descent Algorithm Using the Three-Step Discretization Method for Solving Unconstrained Optimization Problems

Cited by 6 publications

References 34 publications

An Efficient Three-Term Iterative Method for Estimating Linear Approximation Models in Regression Analysis

An Efficient Three-Term Iterative Method for Estimating Linear Approximation Models in Regression Analysis

A note on a Comparison of Starting Points for the Generation of D-optimal Second-order Designs

Descent three-term DY-type conjugate gradient methods for constrained monotone equations with application

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