Conjugate gradient methods form a class of iterative algorithms that are highly effective for solving large‐scale unconstrained optimisation problems. They achieve efficiency by constructing search ...

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 ...

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 ...

This is a preview. Log in through your library . Abstract Under what circumstances can finite difference approximations serve as substitutes for analytical derivatives in unconstrained optimization?

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 ...

A thorough understanding of Linear Algebra and Vector Calculus, and strong familiarity with the Python programming language (e.g., basic data manipulation libraries, how to construct functions and ...

Studies linear and nonlinear programming, the simplex method, duality, sensitivity, transportation and network flow problems, some constrained and unconstrained optimization theory, and the ...

To fulfill the 2 Core Courses, take two Core Courses from two different Core Areas. CSE Core Courses are classified into six areas: Introduction to CSE, Computational Mathematics, High Performance ...

A research team from the University of Lübeck in Germany has made significant progress in the field of database query optimization, utilizing quantum computing technology to markedly enhance the ...

Recently, a research team from the Rudolf Technology Center in Slovenia proposed a new method to optimize the sparse subgraph problem, which has wide applications in fields such as network analysis ...