A particle swarm–BFGS algorithm for nonlinear programming problems

Mohammad-Nezhad, Ali; Shandiz, Roohollah Aliakbari; Jahromi, Abdolhamid Eshraghniaye

doi:10.1016/j.cor.2012.11.008

Cited by 29 publications

(11 citation statements)

References 27 publications

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“…This is the hurdle that the BFGS algorithm solves by looking up rows in the selected direction to determine the optimal distance to move. In-depth information about the BFGS algorithm can be found in studies such as those conducted by Wu et al (2020) and Nezhad et al (2013).…”

Section: Methodsmentioning

confidence: 99%

Ensuring Financial System Sustainability: Combating Hybrid Threats through Anti-Money Laundering and Counter-Terrorist Financing Measures

Korauš,

Jančíková,

Gombár

et al. 2024

JRFM

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This paper deals with ensuring the sustainability of the financial system and combating hybrid threats in relation to anti-money laundering and counter-terrorist financing (AML/CTF) measures. International cooperation in the field of combating hybrid threats is only at the beginning, and in many ways, the experience of international cooperation in the fight against money laundering and terrorist financing, which is based on many years of experience in the institutional and legislative fields, could be used. Hybrid threats are constantly changing and evolving, which means our response to them must also constantly evolve and adapt. The aim of the presented study is the analysis of the problem of the legalization of income from criminal activity and the financing of terrorism and their possible relationship with the fight against hybrid threats and maintaining the stability of the financial system.

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Section: Methodsmentioning

confidence: 99%

Ensuring Financial System Sustainability: Combating Hybrid Threats through Anti-Money Laundering and Counter-Terrorist Financing Measures

Korauš,

Jančíková,

Gombár

et al. 2024

JRFM

View full text Add to dashboard Cite

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“…The BFGS method represents the most efficacious quasi-Newton method used for unrestricted nonlinear schedules in the extensive area of nonlinear programming. In general, BFGS is different form the Newton method, where an assessment to Hessian matrix is assumed instead of the true Hessian Matrix H (Bazaraa et al, 2013;Nezhad et al, 2013). The minimization of the conventional Newton methods can be achieved by calculating the gradient as well as the Hessian matrix of subsequent derivatives when optimizing the function optimization.…”

Section: Broyden Fletcher Goldfarb Shanno Algorithmmentioning

confidence: 99%

Application of soft computing approaches for modeling annular pressure loss of slim-hole wells in one of Iranian central oil fields

et al. 2023

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In order to have a better control over the drilling process and reduce the overall cost of this drilling operation, engineers have tried to use soft computing (SC) techniques to conduct the preestimation of drilling events. It is critically important to estimate the annular pressure losses (APL) for non-Newtonian drilling muds within annulus in order to specify pump rates and also to be able to choose the most appropriate mud pump systems while conducting the drilling operations. To develop the vigorous and exact models to enable the prediction of APL, two popular models were employed, i.e., multilayer perceptron (MLP) [optimized by levenbergmarquardt (LM), bayesian regularization (BR), scaled conjugate gradient (SCG), Resilient back propagation (RB), and broyden fletcher goldfarb shanno (BFGS)] and radial basis function (RBF). Subsequently, applying a committee machine intelligent system (CMIS), the four top models were combined into a unit paradigm. Several tools such as error distribution diagram, cross plot, trend analysis, and cumulative frequency diagram were used in conjunction with statistical calculation to assess the efficiency of models. Consequently, the CMIS model was introduced as the most exact technique which has the greatest coefficient of determination (R 2 close to one) as well as the lowest root mean square error (RMSE close to zero) for the tested dataset.

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“…As seen, the optimization models (15) and 17illustrate NP-hard mixed-integer nonlinear programming problems for which the classical methods are not practically efficient [12]. As known, metaheuristic algorithms have attracted special attention in developing efficiently robust computational procedures for solving a vast variety of such problems [38,24]. These nature-inspired methods are so popular since their softwares can be flexibly reused and also, they can efficiently solve complicated problems even in large scale cases [30,8,38].…”

Section: κ(A)mentioning

confidence: 99%

Two penalized mixed–integer nonlinear programming approaches to tackle multicollinearity and outliers effects in linear regression models

Roozbeh¹,

Babaie-Kafaki²,

Aminifard³

2021

JIMO

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In classical regression analysis, the ordinary least-squares estimation is the best strategy when the essential assumptions such as normality and independency to the error terms as well as ignorable multicollinearity in the covariates are met. However, if one of these assumptions is violated, then the results may be misleading. Especially, outliers violate the assumption of normally distributed residuals in the least-squares regression. In this situation, robust estimators are widely used because of their lack of sensitivity to outlying data points. Multicollinearity is another common problem in multiple regression models with inappropriate effects on the least-squares estimators. So, it is of great importance to use the estimation methods provided to tackle the mentioned problems. As known, robust regressions are among the popular methods for analyzing the data that are contaminated with outliers. In this guideline, here we suggest two mixed-integer nonlinear optimization models which their solutions can be considered as appropriate estimators when the outliers and multicollinearity simultaneously appear in the data set. Capable to be effectively solved by metaheuristic algorithms, the models are designed based on penalization schemes with the ability of down-weighting or ignoring unusual data and multicollinearity effects. We establish that our models are computationally advantageous in the perspective of the flop count. We also deal with a robust ridge methodology. Finally, three real data sets are analyzed to examine performance of the proposed methods.

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A particle swarm–BFGS algorithm for nonlinear programming problems

Cited by 29 publications

References 27 publications

Ensuring Financial System Sustainability: Combating Hybrid Threats through Anti-Money Laundering and Counter-Terrorist Financing Measures

Ensuring Financial System Sustainability: Combating Hybrid Threats through Anti-Money Laundering and Counter-Terrorist Financing Measures

Application of soft computing approaches for modeling annular pressure loss of slim-hole wells in one of Iranian central oil fields

Two penalized mixed–integer nonlinear programming approaches to tackle multicollinearity and outliers effects in linear regression models

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