Search Results - subject:"Local minima"

The sparse(st) optimization problem: reformulations, optimality, stationarity, and numerical results

Kanzow, Christian; Schwartz, Alexandra; Weiß, Felix - In: Computational Optimization and Applications 90 (2024) 1, pp. 77-112

We consider the sparse optimization problem with nonlinear constraints and an objective function, which is given by the sum of a general smooth mapping and an additional term defined by the ℓ0-quasi-norm. This term is used to obtain sparse solutions, but difficult to handle due to its...

Persistent link: https://www.econbiz.de/10015361667

Multi-agent natural actor-critic reinforcement learning algorithms

Trivedi, Prashant; Hemachandra, Nandyala - In: Dynamic games and applications : DGA 13 (2023) 1, pp. 25-55

Persistent link: https://www.econbiz.de/10014225774

An automatic method for generating multiple alignment alternatives for a railway bypass

Vázquez-Méndez, Miguel E.; Casal, Gerardo; Castro, Alberte - In: Computers & operations research : and their … 154 (2023), pp. 1-14

Persistent link: https://www.econbiz.de/10014308255

An Improved Genetic Algorithm for Document Clustering on the Cloud

Akter, Ruksana; Chung, Yoojin - In: International Journal of Cloud Applications and … 8 (2018) 4, pp. 20-28

population, leading it to be trapped in local minima. In this approach, a crossover point may be selected even at a position … local minima, and find better solutions than the traditional approaches. Moreover, instead of running only one genetic …

Persistent link: https://www.econbiz.de/10012043406

Efficient accommodation of local minima in watershed model calibration

Skahill, B. E.; Doherty, J. E. - 2006

The Gauss-Marquardt-Levenberg (GML) method of computer-based parameter estimation, in common with other gradient-based approaches, suffers from the drawback that it may become trapped in local objective function minima, and thus report optimized parameter values that are not, in fact, optimized...

Persistent link: https://www.econbiz.de/10009447949