Novel First Order Optimization Classification Framework

Peter GECZY; Shiro USUI

Novel First Order Optimization Classification Framework

Peter GECZY, Shiro USUI

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Numerous scientific and engineering fields extensively utilize optimization techniques for finding appropriate parameter values of models. Various optimization methods are available for practical use. The optimization algorithms are classified primarily due to the rates of convergence. Unfortunately, it is often the case in practice that the particular optimization method with specified convergence rates performs substantially differently on diverse optimization tasks. Theoretical classification of convergence rates then lacks its relevance in the context of the practical optimization. It is therefore desirable to formulate a novel classification framework relevant to the theoretical concept of convergence rates as well as to the practical optimization. This article introduces such classification framework. The proposed classification framework enables specification of optimization techniques and optimization tasks. It also underlies its inherent relationship to the convergence rates. Novel classification framework is applied to categorizing the tasks of optimizing polynomials and the problem of training multilayer perceptron neural networks.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E83-A No.11 pp.2312-2319

Publication Date: 2000/11/25

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Type of Manuscript: PAPER

Category: Numerical Analysis and Optimization

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Peter GECZY, Shiro USUI, "Novel First Order Optimization Classification Framework" in IEICE TRANSACTIONS on Fundamentals, vol. E83-A, no. 11, pp. 2312-2319, November 2000, doi: .
Abstract: Numerous scientific and engineering fields extensively utilize optimization techniques for finding appropriate parameter values of models. Various optimization methods are available for practical use. The optimization algorithms are classified primarily due to the rates of convergence. Unfortunately, it is often the case in practice that the particular optimization method with specified convergence rates performs substantially differently on diverse optimization tasks. Theoretical classification of convergence rates then lacks its relevance in the context of the practical optimization. It is therefore desirable to formulate a novel classification framework relevant to the theoretical concept of convergence rates as well as to the practical optimization. This article introduces such classification framework. The proposed classification framework enables specification of optimization techniques and optimization tasks. It also underlies its inherent relationship to the convergence rates. Novel classification framework is applied to categorizing the tasks of optimizing polynomials and the problem of training multilayer perceptron neural networks.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e83-a_11_2312/_p

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@ARTICLE{e83-a_11_2312,
author={Peter GECZY, Shiro USUI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Novel First Order Optimization Classification Framework},
year={2000},
volume={E83-A},
number={11},
pages={2312-2319},
abstract={Numerous scientific and engineering fields extensively utilize optimization techniques for finding appropriate parameter values of models. Various optimization methods are available for practical use. The optimization algorithms are classified primarily due to the rates of convergence. Unfortunately, it is often the case in practice that the particular optimization method with specified convergence rates performs substantially differently on diverse optimization tasks. Theoretical classification of convergence rates then lacks its relevance in the context of the practical optimization. It is therefore desirable to formulate a novel classification framework relevant to the theoretical concept of convergence rates as well as to the practical optimization. This article introduces such classification framework. The proposed classification framework enables specification of optimization techniques and optimization tasks. It also underlies its inherent relationship to the convergence rates. Novel classification framework is applied to categorizing the tasks of optimizing polynomials and the problem of training multilayer perceptron neural networks.},
keywords={},
doi={},
ISSN={},
month={November},}

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TY - JOUR
TI - Novel First Order Optimization Classification Framework
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2312
EP - 2319
AU - Peter GECZY
AU - Shiro USUI
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E83-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2000
AB - Numerous scientific and engineering fields extensively utilize optimization techniques for finding appropriate parameter values of models. Various optimization methods are available for practical use. The optimization algorithms are classified primarily due to the rates of convergence. Unfortunately, it is often the case in practice that the particular optimization method with specified convergence rates performs substantially differently on diverse optimization tasks. Theoretical classification of convergence rates then lacks its relevance in the context of the practical optimization. It is therefore desirable to formulate a novel classification framework relevant to the theoretical concept of convergence rates as well as to the practical optimization. This article introduces such classification framework. The proposed classification framework enables specification of optimization techniques and optimization tasks. It also underlies its inherent relationship to the convergence rates. Novel classification framework is applied to categorizing the tasks of optimizing polynomials and the problem of training multilayer perceptron neural networks.
ER -