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 ...

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 ...

This course discusses basic convex analysis (convex sets, functions, and optimization problems), optimization theory (linear, quadratic, semidefinite, and geometric programming; optimality conditions ...

More information: Wenye Ji et al, Recent advances in metasurface design and quantum optics applications with machine learning, physics-informed neural networks, and topology optimization methods, ...

This course introduces high-performance computing (“HPC”) systems, software, and methods used to solve large-scale problems in science and engineering. It will focus on the intersection of two ...

In this talk today we will discuss how to improve the resolution of your current CE-SDS methods. We will cover how CE-SDS works and what types of derivatisation for laser induced fluorescence (LIF) ...

SMi Group reports: Alina Alexeenko of Purdue University presenting LyoHub's overview and strategies for the future of lyophilization and alternatives to conventional methods Optimization of ...