This course discusses basic convex analysis (convex sets, functions, and optimization problems), optimization theory (linear, quadratic, semidefinite, and geometric programming; optimality conditions ...
In this paper we test different conjugate gradient (CG) methods for solving largescale unconstrained optimization problems. The methods are divided in two groups: the first group includes five basic ...
Journal of Computational Mathematics, Vol. 26, No. 2 (March 2008), pp. 227-239 (13 pages) We discuss semiconvergence of the extrapolated iterative methods for solving singular linear systems. We ...
DUBLIN--(BUSINESS WIRE)--Research and Markets(http://www.researchandmarkets.com/research/799091/deterministic_oper) has announced the addition of John Wiley and Sons ...
This course offers an introduction to mathematical nonlinear optimization with applications in data science. The theoretical foundation and the fundamental algorithms for nonlinear optimization are ...
In the ever-evolving world of artificial intelligence, deep neural networks (DNNs) have revolutionized data processing, offering unparalleled accuracy across various ...
The downstream processing of virus particles, vesicles, RNAs, plasmids and other forms of DNA, contains multiple interdependent steps, each requiring optimization for best results. This webinar will ...
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