scholarly journals Stability Score for Local Solutions of Unconstrained Parametric Nonlinear Programs

PAMM ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ivan Mykhailiuk ◽  
Kai Schäfer ◽  
Christof Büskens
Author(s):  
Ercüment H. Ortaçgil

The pseudogroup of local solutions in Chapter 3 defines another pseudogroup by taking its centralizer inside the diffeomorphism group Diff(M) of a manifold M. These two pseudogroups define a Lie group structure on M.


Nature Food ◽  
2021 ◽  
Author(s):  
Philip M. Haygarth ◽  
Mariana C. Rufino
Keyword(s):  

Urban Science ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 24
Author(s):  
Gordon F. Mulligan ◽  
John I. Carruthers

This paper examines the joint adjustment of population and employment numbers across America’s metropolitan areas during the period 1990–2015. Current levels of both are estimated, for 10 year periods, using their lagged (own and cross) levels and eight other lagged variables. Population is affected by both human and natural amenities and employment by wages, patents, and other attributes of the workforce. This paper questions the conventional interpretation of the adjustment process by using geographically weighted regression (GWR) instead of standard linear (OLS, 2GLS) regression. Here the various estimates are all local, so the long-run equilibrium solutions for the adjustment process vary over space. Convergence no longer indicates a stable universal solution but instead involves a mix of stable and unstable local solutions. Local sustainability becomes an issue when making projections because employment can quickly lead or lag population in some metropolitan labor markets.


2008 ◽  
Vol 12 (02) ◽  
pp. 233-248
Author(s):  
Margie Parikh

Dilip Roy is a country head at Itsun Heavy Industry (India) Pvt. Ltd. (IHIIPL) in Delhi, India. It is a wholly owned subsidiary of Itsun China, a leading private sector construction equipment company. Dilip graduated as a mechanical engineer with reputed National Science Talent Search Scholarship, started his career as a Graduate Trainee Engineer and became a Vice President in another company before he joined IHIIPL as a country head. Hu, the representative of Itsun China in India was exploring the Indian market and he ended up offering a job to Dilip after a series of interactions concerning the Indian Construction Equipment Industry. This was the first opportunity for Dilip to head an entire company. He knew the industry thoroughly and felt excited that finally his ambition was at the verge of fulfillment. When Dilip joined, IHIPL had yet to be incorporated though some business activities had started. Dilip's time at IHIIPL is dotted with problem after problem. The key problems encountered were confusion about reporting relationships, unresponsive head office with its unilateral decisions, and unprofessional and incompetent colleagues. Dilip had taken steps to address most of the company's problems: developing local solutions, drawing on personal resources, and hiring new staff. The business was growing fast on the back of increasing demand. Subsequently, Dilip realized that he was not considered trustworthy by the head office and was not involved in major decisions. His initial perception about his job and IHIIPL changed. Even though he was later given the certificate of honor with an invitation to attend the award ceremony in China, he left the company. This case is useful for examining the issues of cross-cultural management and leadership.


2016 ◽  
Vol 25 (06) ◽  
pp. 1650033 ◽  
Author(s):  
Hossam Faris ◽  
Ibrahim Aljarah ◽  
Nailah Al-Madi ◽  
Seyedali Mirjalili

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets.


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