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Related Experiment Video

Updated: Jun 8, 2026

Computerized Adaptive Testing System of Functional Assessment of Stroke

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

Efficient Controls for Finitely Convergent Sequential Algorithms.

Wei Chen¹, Gabor T Herman

¹Department of Computer Science, Graduate Center, City University of New York, 365 Fifth Avenue, New York, NY 10016, USA; wchen@gc.cuny.edu.

ACM Transactions on Mathematical Software. Association for Computing Machinery

|October 19, 2010

Summary

This summary is machine-generated.

This study introduces a method to transform sequential algorithms, like ART3, for solving feasibility problems. The new approach retains finite convergence while improving speed, offering faster solutions for scientific computing tasks.

Related Experiment Videos

Last Updated: Jun 8, 2026

Computerized Adaptive Testing System of Functional Assessment of Stroke

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

Area of Science:

Computational mathematics
Numerical analysis
Optimization theory

Background:

Feasibility problems are fundamental in scientific computing, requiring algorithms to find points satisfying constraints.
Existing algorithms like ART3 (a cyclic sequential algorithm) solve these problems but can be improved.
Previous work introduced a faster, non-cyclic variant of ART3, demonstrating potential for enhanced convergence.

Purpose of the Study:

To propose a general methodology for automatically transforming finitely convergent sequential algorithms.
To ensure that transformed algorithms maintain finite convergence.
To enhance the speed of convergence for these algorithms in practical applications.

Main Methods:

Developing a general transformation methodology for sequential algorithms.
Proving the retention of finite convergence through mathematical theorems.
Applying transformed algorithms to a practical problem to demonstrate improved convergence speed.

Main Results:

A novel methodology for algorithm transformation has been successfully proposed.
Mathematical proofs confirm that finite convergence is preserved post-transformation.
Empirical results show that the transformed algorithms typically converge faster than their original counterparts.

Conclusions:

The proposed methodology offers a systematic way to improve existing convergent sequential algorithms.
Finite convergence is mathematically guaranteed, ensuring reliability.
The enhanced algorithms present a practical advantage in terms of computational efficiency for feasibility problems.