Role of line search in least-squares optimization of lens design

1994 ◽  
Vol 33 (12) ◽  
pp. 4060 ◽  
Author(s):  
Lakshminarayan Hazra
Keyword(s):  
Least Squares ◽  
Line Search ◽  
Lens Design ◽  
Least Squares Optimization

Total Hessian in Least Squares Optimization of Lens Design

2000 ◽  
Vol 29 (2) ◽  
pp. 95-104
Author(s):  
J. Basu ◽  
L. N. Hazra
Keyword(s):  
Least Squares ◽  
Lens Design ◽  
Least Squares Optimization

scholarly journals The Role of Managerial Commitment and TPM Implementation Strategies in Productivity Benefits

2018 ◽  
Vol 8 (7) ◽  
pp. 1153 ◽  
Author(s):  
José Díaz-Reza ◽  
Jorge García-Alcaraz ◽  
Liliana Avelar-Sosa ◽  
José Mendoza-Fong ◽  
Juan Sáenz Diez-Muro ◽  
...  
Keyword(s):  
Least Squares ◽  
Structural Equation Model ◽  
Preventive Maintenance ◽  
Latent Variables ◽  
Structural Equation ◽  
Manufacturing Industry ◽  
Implementation Strategies ◽  
Equation Model ◽  
Total Productive Maintenance

The present research proposes a structural equation model to integrate four latent variables: managerial commitment, preventive maintenance, total productive maintenance, and productivity benefits. In addition, these variables are related through six research hypotheses that are validated using collected data from 368 surveys administered in the Mexican manufacturing industry. Consequently, the model is evaluated using partial least squares. The results show that managerial commitment is critical to achieve productivity benefits, while preventive maintenance is indispensable to total preventive maintenance. These results may encourage company managers to focus on managerial commitment and implement preventive maintenance programs to guarantee the success of total productive maintenance.

scholarly journals Initial coin offerings and the cryptocurrency hype - the moderating role of exogenous and endogenous signals

2021 ◽  
Author(s):  
Ferdinand Thies ◽  
Sören Wallbach ◽  
Michael Wessel ◽  
Markus Besler ◽  
Alexander Benlian
Keyword(s):  
Least Squares ◽  
Ordinary Least Squares ◽  
High Quality ◽  
Academic Interest ◽  
Entrepreneurial Ventures ◽  
Asset Class ◽  
The Impact ◽  
Moderating Role ◽  
Initial Coin Offerings

AbstractInitial coin offerings (ICOs) have recently emerged as a new financing instrument for entrepreneurial ventures, spurring economic and academic interest. Nevertheless, the impact of exogenous and endogenous signals on the performance of ICOs as well as the effects of the cryptocurrency hype and subsequent downfall of Bitcoin between 2016 and 2019 remain underexplored. We applied ordinary least squares (OLS) regressions based on a dataset containing 1597 ICOs that covers almost 2.5 years. The results show that exogenous and endogenous signals have a significant effect on the funds raised in ICOs. We also find that the Bitcoin price heavily drives the performance of ICOs. However, this hype effect is moderated, as high-quality ICOs are not pegged to these price developments. Revealing the interplay between hypes and signals in the ICO’s asset class should broaden the discussion of this emerging digital phenomenon.

scholarly journals Method for the identification of explicit polynomial formulae for the friction in turbulent pipe flow

1999 ◽  
Vol 1 (2) ◽  
pp. 115-126 ◽  
Author(s):  
J. W. Davidson ◽  
D. Savic ◽  
G. A. Walters
Keyword(s):  
Least Squares ◽  
Symbolic Regression ◽  
Regression Method ◽  
Turbulent Pipe Flow ◽  
Model Complexity ◽  
Improve Performance ◽  
Rule Based ◽  
Least Squares Optimization ◽  
Starting Solutions ◽  
Crossover And Mutation

The paper describes a new regression method for creating polynomial models. The method combines numerical and symbolic regression. Genetic programming finds the form of polynomial expressions, and least squares optimization finds the values for the constants in the expressions. The incorporation of least squares optimization within symbolic regression is made possible by a rule-based component that algebraically transforms expressions to equivalent forms that are suitable for least squares optimization. The paper describes new operators of crossover and mutation that improve performance, and a new method for creating starting solutions that avoids the problem of under-determined functions. An example application demonstrates the trade-off between model complexity and accuracy of a set of approximator functions created for the Colebrook–White formula.

Sign in / Sign up

Export Citation Format

Share Document