We propose a new nonmonotone filter method to promote global and fast local convergence for sequential quadratic programming algorithms. Our method uses two filters: a standard, global g-filter for global convergence, and a local nonmonotone l-filter that allows us to establish fast local...

The measure of the regional disparities in the European Union was widely approached on the literature. Recent years have seen a spate of studies taking into account the spatial aspect of data in the analysis of convergence from an empirical viewpoint, or integrating spatial interdependencies...

<Para ID="Par1">We present a local convergence analysis of Gauss-Newton method for solving nonlinear least square problems. Using more precise majorant conditions than in earlier studies such as Chen (Comput Optim Appl 40:97–118, <CitationRef CitationID="CR8">2008</CitationRef>), Chen and Li (Appl Math Comput 170:686–705, <CitationRef CitationID="CR9">2005</CitationRef>), Chen and Li (Appl...</citationref></citationref></para>

In this work we propose a class of quasi-Newton methods to minimize a twice differentiable function with Lipschitz continuous Hessian. These methods are based on the quadratic regularization of Newton’s method, with algebraic explicit rules for computing the regularizing parameter. The...

In the last decade, after accession to European Union, Poland has achieved a significant improvement of indicators relating to income on a national level. Polish GDP per capita (in PPP terms) increased from 49% of European Union average in 2004 to 67% in 2013 showing progress in each year...

In this article, the authors propose a new distributed least-squares algorithm to address the sensor fusion problem in using wireless sensor networks (WSN) to monitor the behaviors of large-scale multiagent systems. Under a mild assumption on network observability, that is, each sensor can take...

Strong consistency of least squares estimators of the slope parameter in simple linear regression models is established for predetermined stochastic regressors. The main result covers a class of models which falls outside the applicability of what is presently available in the literature. An...