Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Vuong, Phan T."'
We study differentiable strongly quasiconvex functions for providing new properties for algorithmic and monotonicity purposes. Furthemore, we provide insights into the decreasing behaviour of strongly quasiconvex functions, applying this for establis
Externí odkaz:
http://arxiv.org/abs/2410.03534
The Boosted Difference of Convex functions Algorithm (BDCA) has been recently introduced to accelerate the performance of the classical Difference of Convex functions Algorithm (DCA). This acceleration is achieved thanks to an extrapolation step from
Externí odkaz:
http://arxiv.org/abs/1908.01138
The Difference of Convex functions Algorithm (DCA) is widely used for minimizing the difference of two convex functions. A recently proposed accelerated version, termed BDCA for Boosted DC Algorithm, incorporates a line search step to achieve a large
Externí odkaz:
http://arxiv.org/abs/1907.11471
The Boosted Difference of Convex functions Algorithm (BDCA) was recently proposed for minimizing smooth difference of convex (DC) functions. BDCA accelerates the convergence of the classical Difference of Convex functions Algorithm (DCA) thanks to an
Externí odkaz:
http://arxiv.org/abs/1812.06070
Akademický článek
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We present two globally convergent Levenberg-Marquardt methods for finding zeros of H\"{o}lder metrically subregular mappings that may have non-isolated zeros. The first method unifies the Levenberg- Marquardt direction and an Armijo-type line search
Externí odkaz:
http://arxiv.org/abs/1812.00818
Autor:
Heirendt, Laurent, Arreckx, Sylvain, Pfau, Thomas, Mendoza, Sebastián N., Richelle, Anne, Heinken, Almut, Haraldsdóttir, Hulda S., Wachowiak, Jacek, Keating, Sarah M., Vlasov, Vanja, Magnusdóttir, Stefania, Ng, Chiam Yu, Preciat, German, Žagare, Alise, Chan, Siu H. J., Aurich, Maike K., Clancy, Catherine M., Modamio, Jennifer, Sauls, John T., Noronha, Alberto, Bordbar, Aarash, Cousins, Benjamin, Assal, Diana C. El, Valcarcel, Luis V., Apaolaza, Iñigo, Ghaderi, Susan, Ahookhosh, Masoud, Guebila, Marouen Ben, Kostromins, Andrejs, Sompairac, Nicolas, Le, Hoai M., Ma, Ding, Sun, Yuekai, Wang, Lin, Yurkovich, James T., Oliveira, Miguel A. P., Vuong, Phan T., Assal, Lemmer P. El, Kuperstein, Inna, Zinovyev, Andrei, Hinton, H. Scott, Bryant, William A., Artacho, Francisco J. Aragón, Planes, Francisco J., Stalidzans, Egils, Maass, Alejandro, Vempala, Santosh, Hucka, Michael, Saunders, Michael A., Maranas, Costas D., Lewis, Nathan E., Sauter, Thomas, Palsson, Bernhard Ø., Thiele, Ines, Fleming, Ronan M. T.
COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Too
Externí odkaz:
http://arxiv.org/abs/1710.04038
We describe and analyse Levenberg-Marquardt methods for solving systems of nonlinear equations. More specifically, we propose an adaptive formula for the Levenberg-Marquardt parameter and analyse the local convergence of the method under H\"{o}lder m
Externí odkaz:
http://arxiv.org/abs/1703.07461
We introduce two new algorithms to minimise smooth difference of convex (DC) functions that accelerate the convergence of the classical DC algorithm (DCA). We prove that the point computed by DCA can be used to define a descent direction for the obje
Externí odkaz:
http://arxiv.org/abs/1507.07375
Akademický článek
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