Voronoĭ’s memoir of 1903: “On a problem from the theory of asymptotic functions”

2005 ◽  
pp. 165-168
Keyword(s):  
Asymptotic Functions

scholarly journals An embedding of Schwartz distributions in the algebra of asymptotic functions

1998 ◽  
Vol 21 (3) ◽  
pp. 417-428 ◽  
Author(s):  
Michael Oberguggenberger ◽  
Todor Todorov
Keyword(s):  
Differential Algebra ◽  
Generalized Functions ◽  
Schwartz Distributions ◽  
Asymptotic Functions ◽  
Space Of Distributions ◽  
Asymptotic Function

We present a solution of the problem of multiplication of Schwartz distributions by embedding the space of distributions into a differential algebra of generalized functions, called in the paper “asymptotic function,” similar to but different from J. F Colombeau's algebras of new generalized functions.

A Further Study on Asymptotic Functions via Variational Analysis

2019 ◽  
Vol 182 (1) ◽  
pp. 366-382 ◽  
Author(s):  
Felipe Lara ◽  
Rubén López ◽  
Benar F. Svaiter
Keyword(s):  
Variational Analysis ◽  
Asymptotic Functions

On Asymptotic Functions

1980 ◽  
Vol s2-21 (2) ◽  
pp. 297-303 ◽  
Author(s):  
Gábor Somorjai
Keyword(s):  
Asymptotic Functions

scholarly journals Estimation of CN Parameter for Small Agricultural Watersheds Using Asymptotic Functions

Water ◽  
2015 ◽  
Vol 7 (12) ◽  
pp. 939-955 ◽  
Author(s):  
Tomasz Kowalik ◽  
Andrzej Walega
Keyword(s):  
Agricultural Watersheds ◽  
Asymptotic Functions

scholarly journals RHOASo: An Early Stop Hyper-Parameter Optimization Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2334
Author(s):  
Ángel Luis Muñoz Castañeda ◽  
Noemí DeCastro-García ◽  
David Escudero García
Keyword(s):  
Parameter Optimization ◽  
Learning Algorithm ◽  
Optimization Algorithms ◽  
The Other ◽  
Machine Learning Algorithm ◽  
Asymptotic Functions ◽  
Benchmark Datasets ◽  
Tuning Process ◽  
Number Of Iterations ◽  
Conditional Optimization

This work proposes a new algorithm for optimizing hyper-parameters of a machine learning algorithm, RHOASo, based on conditional optimization of concave asymptotic functions. A comparative analysis of the algorithm is presented, giving particular emphasis to two important properties: the capability of the algorithm to work efficiently with a small part of a dataset and to finish the tuning process automatically, that is, without making explicit, by the user, the number of iterations that the algorithm must perform. Statistical analyses over 16 public benchmark datasets comparing the performance of seven hyper-parameter optimization algorithms with RHOASo were carried out. The efficiency of RHOASo presents the positive statistically significant differences concerning the other hyper-parameter optimization algorithms considered in the experiments. Furthermore, it is shown that, on average, the algorithm needs around 70% of the iterations needed by other algorithms to achieve competitive performance. The results show that the algorithm presents significant stability regarding the size of the used dataset partition.

scholarly journals Entire Functions with Asymptotic Functions.

1995 ◽  
Vol 77 ◽  
pp. 153
Author(s):  
A. Hinkkanen ◽  
John Rossi
Keyword(s):  
Entire Functions ◽  
Asymptotic Functions
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