scholarly journals A coevolution archive based on problem dimension

2008 ◽  
Author(s):  
Liping Yang ◽  
Houkuan Huang ◽  
Yabin Liu
Keyword(s):  
Problem Dimension

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

scholarly journals On the Locally Polynomial Complexity of the Projection-Gradient Method for Solving Piecewise Quadratic Optimisation Problems

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 465
Author(s):  
Agnieszka Prusińska ◽  
Krzysztof Szkatuła ◽  
Alexey Tret’yakov
Keyword(s):  
Computational Complexity ◽  
Polynomial Complexity ◽  
Initial Point ◽  
Quadratic Functions ◽  
Problem Dimension ◽  
Piecewise Quadratic Functions ◽  
Large Systems ◽  
Systems Of Linear Inequalities ◽  
Number Of Iterations ◽  
Finite Number Of Iterations

This paper proposes a method for solving optimisation problems involving piecewise quadratic functions. The method provides a solution in a finite number of iterations, and the computational complexity of the proposed method is locally polynomial of the problem dimension, i.e., if the initial point belongs to the sufficiently small neighbourhood of the solution set. Proposed method could be applied for solving large systems of linear inequalities.

scholarly journals Generalized Green's functions for higher order boundary value matrix differential systems

1992 ◽  
Vol 15 (3) ◽  
pp. 523-535 ◽  
Author(s):  
R. J. Villanueva ◽  
L. Jodar
Keyword(s):  
Boundary Value ◽  
Existence Condition ◽  
Higher Order ◽  
Closed Form Expression ◽  
Problem Dimension ◽  
Green’S Matrix ◽  
Matrix Differential ◽  
Matrix Problems ◽  
Differential Matrix ◽  
Well Posed

In this paper, a Green's matrix function for higher order two point boundary value differential matrix problems is constructed. By using the concept of rectangular co-solution of certain algebraic matrix equation associated to the problem, an existence condition as well as an explicit closed form expression for the solution of possibly not well-posed boundary value problems is given avoiding the increase of the problem dimension.

scholarly journals Solving higher order Fuchs type differential systems avoiding the increase of the problem dimension

1994 ◽  
Vol 17 (1) ◽  
pp. 91-102
Author(s):  
E. Navarro ◽  
L. Jódar ◽  
R. Company
Keyword(s):  
Homogeneous Problem ◽  
Higher Order ◽  
Closed Form Expression ◽  
Power Series Solution ◽  
Matrix Equations ◽  
Form Expression ◽  
Initial Value ◽  
Generalized Power Series ◽  
The Matrix ◽  
Problem Dimension

In this paper, we develop a Frobenius matrix method for solving higher order systems of differential equations of the Fuchs type. Generalized power series solution of the problem are constructed without increasing the problem dimension. Solving appropriate algebraic matrix equations a closed form expression for the matrix coefficient of the series are found. By means of the concept of ak-fundamental set of solutions of the homogeneous problem an explicit solution of initial value problems are given.

OPTIMAL DESIGN OF STRUCTURES USING THE SIMULTANEOUS PERTURBATION STOCHASTIC APPROXIMATION ALGORITHM

2009 ◽  
Vol 06 (02) ◽  
pp. 229-245 ◽  
Author(s):  
S. FALLAHIAN ◽  
D. HAMIDIAN ◽  
S. M. SEYEDPOOR
Keyword(s):  
Optimal Design ◽  
Stochastic Approximation ◽  
Optimization Problem ◽  
Computational Cost ◽  
Global Optimum ◽  
The Other ◽  
Optimization Process ◽  
Simultaneous Perturbation Stochastic Approximation ◽  
Stochastic Nature ◽  
Problem Dimension

This paper presents an application of the simultaneous perturbation stochastic approximation (SPSA) algorithm to optimization of structures. This method requires only two structural analyses in each cycle of optimization process, regardless of optimization problem dimension. This characteristic is very promising in reduction of computational cost of optimization process, especially in problems with a large number of variables to be optimized. Furthermore, the stochastic nature of the SPSA can enhance the convergence of the method to achieve the global optimum. In order to assess the effectiveness of the proposed method some benchmark truss examples are considered. The numerical results demonstrate the competence of the method in comparison with the other methods found in the literature.

scholarly journals Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning

2019 ◽  
Author(s):  
Lingxiao Wang ◽  
Quanquan Gu
Keyword(s):  
Logistic Regression ◽  
Linear Regression ◽  
Synthetic Data ◽  
Selection Procedure ◽  
Linear Convergence ◽  
Sparse Learning ◽  
Learning Problem ◽  
Sparse Logistic Regression ◽  
Problem Dimension ◽  
Real World Datasets

We consider the differentially private sparse learning problem, where the goal is to estimate the underlying sparse parameter vector of a statistical model in the high-dimensional regime while preserving the privacy of each training example. We propose a generic differentially private iterative gradient hard threshoding algorithm with a linear convergence rate and strong utility guarantee. We demonstrate the superiority of our algorithm through two specific applications: sparse linear regression and sparse logistic regression. Specifically, for sparse linear regression, our algorithm can achieve the best known utility guarantee without any extra support selection procedure used in previous work \cite{kifer2012private}. For sparse logistic regression, our algorithm can obtain the utility guarantee with a logarithmic dependence on the problem dimension.  Experiments on both synthetic data and real world datasets verify the effectiveness of our proposed algorithm.

A Meshless Approach in Solution of Multiscale Solidification Modeling

2010 ◽  
Vol 649 ◽  
pp. 211-216 ◽  
Author(s):  
Božidar Šarler ◽  
Gregor Kosec ◽  
Agnieszka Lorbicka ◽  
Robert Vertnik
Keyword(s):  
Radial Basis Functions ◽  
Phase Field ◽  
Solution Procedure ◽  
Basis Functions ◽  
Governing Equations ◽  
New Approach ◽  
Solidification Modeling ◽  
Time Stepping ◽  
Problem Dimension ◽  
Transfer Problems

This paper describes an overview of a new meshless solution procedure for calculation of one-domain coupled macroscopic heat, mass, momentum and species transfer problems as well as phase-field concepts of grain evolution. The solution procedure is defined on the macro [1] as well as on the micro levels [2] by a set of nodes which can be non-uniformly distributed. The domain and boundary of interest are divided into overlapping influence areas. On each of them, the fields are represented by the multiquadrics radial basis functions (RBF) collocation on a related sub-set of nodes. The time-stepping is performed in an explicit way. All governing equations are solved in their strong form, i.e. no integrations are performed. The polygonisation is not present and the formulation of the method is practically independent of the problem dimension. The solution can be easily and efficiently adapted in node redistribution and/or refinement sense, which is of utmost importance when coping with fields exhibiting sharp gradients. The concept and the results of the multiscale solidification modeling with the new approach are compared with the classical mesh-based [3] approach. The method turns out to be extremely simple to code and accurate, inclusion of the complicated physics can easily be looked over. The coding in 2D or 3D is almost identical.

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