Research on the Properties of Coaxality Error Objective Function

2009 ◽  
Vol 16-19 ◽  
pp. 1164-1168 ◽  
Author(s):  
Ping Liu ◽  
San Yang Liu
Keyword(s):  
Euclidean Space ◽  
Objective Function ◽  
Optimization Model ◽  
Dimensional Euclidean Space ◽  
Modern Theory ◽  
Minimal Point ◽  
Zone Method ◽  
Deviation Measurement ◽  
Minimum Zone ◽  
Theoretical Results

The unconstrained optimization model applying to radial deviation measurement is established for assessing coaxality errors by the positioned minimum zone method. The properties of the objective function in the optimization model are thoroughly researched. On the basis of the modern theory of convex functions, it is strictly proved that the objective function is a continuous and non-differentiable and convex function defined on the four-dimensional Euclidean space R4. Therefore, the global minimal value of the objective function is unique and any of its minimal point must be its global minimal point. Thus, any existing optimization algorithm, as long as it is convergent, can be used to solve the objective function to get the wanted values of coaxality errors by the positioned minimum zone assessment. An example is given to verify the theoretical results presented.

Mathematical Model for Evaluating Cylindricity Errors by Minimum Zone Method

2011 ◽  
Vol 284-286 ◽  
pp. 434-438 ◽  
Author(s):  
Ping Liu ◽  
Hui Yi Miao
Keyword(s):  
Mathematical Model ◽  
Objective Function ◽  
Optimization Model ◽  
Dimensional Euclidean Space ◽  
Modern Theory ◽  
Minimal Point ◽  
Zone Method ◽  
Deviation Measurement ◽  
Minimum Zone ◽  
Theoretical Results

An unconstrained optimization model is established for assessing cylindricity errors by the minimum zone method based on radial deviation measurement. The properties of the objective function in the optimization model are thoroughly researched. On the basis of the modern theory on convex functions, it is strictly proved that the objective function is a continuous and non-differentiable and convex function defined on a subset of the four-dimensional Euclidean space. The minimal value of the objective function is unique and any of its minimal point must be its global minimal point. Thus, any existing optimization algorithm, so long as it is convergent, can be applied to solve the objective function in order to get the wanted cylindricity errors by the minimun zone assessment. An example is given to verify the theoretical results presented.

Some Properties of Mathematical Model for Cylindricity Errors

2010 ◽  
Vol 37-38 ◽  
pp. 1214-1218 ◽  
Author(s):  
Ping Liu ◽  
Hui Yi Miao
Keyword(s):  
Mathematical Model ◽  
Euclidean Space ◽  
Convex Function ◽  
Objective Function ◽  
Optimization Model ◽  
Dimensional Euclidean Space ◽  
Modern Theory ◽  
Minimal Point ◽  
Deviation Measurement ◽  
Theoretical Results

An unconstrained optimization model, applicable to radial deviation measurement, is established for assessing cylindricity errors by the minimum circumscribed cylinder evaluation. The properties of the objective function in the optimization model are thoroughly investigated. On the basis of the modern theory of convex functions, it is strictly proved that the objective function is a continuous and non-differentiable and convex function defined on the four-dimensional Euclidean space R4. Therefore, the minimal value of the objective function is unique and any of its minimal point must be its global minimal point. Thus, any existing optimization algorithm, so long as it is convergent, can be used to solve the objective function to get the wanted values of cylindricity errors by the minimum circumscribed cylinder assessment. An example is given to verify the theoretical results presented.

Mathematical Model for Evaluating Cylindricity Errors by Maximum Inscribed Cylinder

2011 ◽  
Vol 474-476 ◽  
pp. 1418-1422
Author(s):  
Ping Liu ◽  
Hui Yi Miao
Keyword(s):  
Mathematical Model ◽  
Euclidean Space ◽  
Convex Function ◽  
Objective Function ◽  
Optimization Model ◽  
Dimensional Euclidean Space ◽  
Modern Theory ◽  
Minimal Point ◽  
Deviation Measurement ◽  
Theoretical Results

An unconstrained optimization model applicable to radial deviation measurement is established for assessing cylindricity errors by the maximum inscribed cylinder evaluation. The properties of the objective function in the optimization model are thoroughly researched. On the basis of the modern theory of convex functions, it is strictly proved that the objective function is a continuous and non-differentiable and convex function defined on a subset of the four-dimensional Euclidean space. The minimal value of the objective function is unique and any of its minimal point must be its global minimal point. Any existing optimization algorithm, so long as it is convergent, can be used to solve the objective function to get the wanted values of cylindricity errors by the maximum inscribed cylinder assessment. An example is given to verify the theoretical results presented.

Mathematical Model for Evaluating Roundness Errors by Minimum Circumscribed Circle Method

2011 ◽  
Vol 328-330 ◽  
pp. 380-383 ◽  
Author(s):  
Ping Liu ◽  
Hui Yi Miao
Keyword(s):  
Mathematical Model ◽  
Objective Function ◽  
Optimization Model ◽  
Circle Method ◽  
Dimensional Euclidean Space ◽  
Modern Theory ◽  
Two Dimensional ◽  
Minimal Point ◽  
Deviation Measurement ◽  
Theoretical Results

An unconstrained optimization model is established for assessing roundness errors by the minimum circumscribed circle method based on radial deviation measurement. The properties of the objective function in the optimization model are thoroughly researched. On the basis of the modern theory on convex functions, it is strictly proved that the objective function is a continuous and non-differentiable and convex function defined on the two-dimensional Euclidean space. The minimal value of the objective function is unique and any of its minimal point must be its global minimal point. Any existing optimization algorithm, so long as it is convergent, can be used to solve the objective function in order to get the wanted roundness errors by the minimun circumscribed circle assessment. One example is given to verify the theoretical results presented.

Mathematical Model for Evaluating Roundness Errors by Maximum Inscribed Circle Method

2011 ◽  
Vol 314-316 ◽  
pp. 393-396 ◽  
Author(s):  
Ping Liu ◽  
Hui Yi Miao
Keyword(s):  
Mathematical Model ◽  
Objective Function ◽  
Optimization Model ◽  
Circle Method ◽  
Dimensional Euclidean Space ◽  
Modern Theory ◽  
Two Dimensional ◽  
Minimal Point ◽  
Deviation Measurement ◽  
Theoretical Results

An unconstrained optimization model is established for assessing roundness errors by the maximum inscribed circle method based on radial deviation measurement. The properties of the objective function in the optimization model are thoroughly researched. On the basis of the modern theory on convex functions, it is strictly proved that the objective function is a continuous and non-differentiable and convex function defined on the two-dimensional Euclidean space. The minimal value of the objective function is unique and any of its minimal point must be its global minimal point. Any existing optimization algorithm, so long as it is convergent, can be used to solve the objective function in order to get the wanted roundness errors by the maximum inscribed circle assessment. One example is given to verify the theoretical results presented.

scholarly journals Optimization In A Finite-Dimensional Euclide Space

2020 ◽  
Vol 7 (1) ◽  
pp. 20-28
Author(s):  
A.I. Kosolap ◽  
Keyword(s):  
Euclidean Space ◽  
Objective Function ◽  
Optimization Model ◽  
Optimization Problems ◽  
Complexity Class ◽  
Feasible Region ◽  
Complexity Classes ◽  
The Third ◽  
Primal Dual ◽  
Multimodal Problems

In this paper, optimization models in Euclidean space are divided into four complexity classes. Ef-fective algorithms have been developed to solve the problems of the first two classes of complexity. These are the primal-dual interior-point methods. Discrete and combinatorial optimization problems of the third complexity class are recommended to be converted to the fourth complexity class with continuous change of variables. Effective algorithms have not been developed for problems of the third and fourth complexity classes, with the exception of a narrow class of problems that are unimodal. The general optimization problem is formulated as a minimum (maximum) objective function in the presence of constraints. The complexity of the problem depends on the structure of the objective function and its feasible region. If the functions that determine the optimization model are quadratic or polynomial, then semidefinite programming can be used to obtain estimates of so-lutions in such problems. Effective methods have been developed for semidefinite optimization problems. Sometimes it’s enough to develop an algorithm without building a mathematical model. We see such an example when sorting an array of numbers. Effective algorithms have been devel-oped to solve this problem. In the work for sorting problems, an optimization model is constructed, and it coincides with the model of the assignment problem. It follows from this that the sorting problem is unimodal. Effective algorithms have not been developed to solve multimodal problems. The paper proposes a simple and effective algorithm for the optimal allocation of resources in mul-tiprocessor systems. This problem is multimodal. In the general case, for solving multimodal prob-lems, a method of exact quadratic regularization is proposed. This method has proven its compara-tive effectiveness in solving many test problems of various dimensions. Keywords: Euclidean space, optimization, unimodal problems, multimodal problems, complexity classes, numerical methods.

COMPARISON OF TWO OPTIMIZATION MODELS FOR CONSTRUCTING PATTERNS IN THE METHOD OF LOGICAL ANALYSIS OF DATA

2020 ◽  
pp. 10-13
Author(s):  
Р.И. Кузьмич ◽  
А.А. Ступина ◽  
С.Н. Ежеманская ◽  
А.П. Шугалей
Keyword(s):  
Objective Function ◽  
Optimization Model ◽  
Logical Analysis ◽  
Optimization Models ◽  
Logical Analysis Of Data

Предлагаются две оптимизационные модели для построения информативных закономерностей. Приводится эмпирическое подтверждение целесообразности использования критерия бустинга в качестве целевой функции оптимизационной модели для получения информативных закономерностей. Информативность, закономерность, критерий бустинга, оптимизационная модель Comparison of two optimization models for constructing patterns in the method of logical analysis of data Two optimization models for constructing informative patterns are proposed. An empirical confirmation of the expediency of using the boosting criterion as an objective function of the optimization model for obtaining informative patterns is given.

On Vitali's Theorem for Groups of Motions of Euclidean Space

1999 ◽  
Vol 6 (4) ◽  
pp. 323-334
Author(s):  
A. Kharazishvili
Keyword(s):  
Euclidean Space ◽  
Dimensional Euclidean Space ◽  
Finite Dimensional

Abstract We give a characterization of all those groups of isometric transformations of a finite-dimensional Euclidean space, for which an analogue of the classical Vitali theorem [Sul problema della misura dei gruppi di punti di una retta, 1905] holds true. This characterization is formulated in purely geometrical terms.

Some properties of Wigner coefficients and hyperspherical harmonics

1956 ◽  
Vol 52 (3) ◽  
pp. 424-430 ◽  
Author(s):  
A. P. Stone
Keyword(s):  
Angular Momentum ◽  
Euclidean Space ◽  
Rotation Group ◽  
Dimensional Euclidean Space ◽  
Hyperspherical Harmonics ◽  
Shift Operators ◽  
Analytical Relations

ABSTRACTGeneral shift operators for angular momentum are obtained and applied to find closed expressions for some Wigner coefficients occurring in a transformation between two equivalent representations of the four-dimensional rotation group. The transformation gives rise to analytical relations between hyperspherical harmonics in a four-dimensional Euclidean space.

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