Igor Griva

Numerical Analysis I / Numerical Methods

The course covers the following topics: floating point arithmetic, solving nonlinear equations in one variable, solving systems of linear equations, solving nonlinear systems, interpolation and polynomial approximation, curve-fitting; cubic and Bezier splines, least squares problems.

• Schedule: MW 12:00 - 1:15 pm, East Building, rm 201
• Office Hours: MW 2:15-3:15 pm, Exploratory Hall, 4114
• Fall 2023

COMUTATIONAL LEARNING AND DISCOVERY

The course surveys algorithms of machine (computational) learning. The main goal of this class is to familiarize students with basic concepts and algorithms. Students who complete this course should be able to identify problems where computational learning algorithms can be useful and to apply these algorithms for finding the solution. We discuss the following topics: parametric/non-parametric learning, decision tree learning, neural networks, Bayesian learning, instance-based learning, Vapnik-Chernovenkis theory, support vector machines, and reinforcement learning. The class provides some necessary background introducing basic concepts from statistics, optimization, and information theory, relevant to computational learning.

• Schedule: T 7:20-10:00 pm, Exploratory Hall 4106
• Office Hours: T 10:00 - 11:00 pm, Exploratory Hall, Rm 4106
• Fall 2023

The Department of Mathematical Sciences

Department of Mathematical Sciences
4400 University Drive, MS: 3F2
Exploratory Hall, room 4400
Fairfax, Virginia 22030
Phone Number: 703-993-1460
Fax Number: 703-993-1491

The Center for Simulation and Modelling

Researchers in the Computational Materials Science Center focus on the discovery, interpretation, simulation, and organization of the microscopic interactions between atoms and molecules in condensed phases of materials including biomaterials. The ability to predict materials properties is a fundamental requirement of technological advances and economic competitiveness.

Linear and Nonlinear Optimization

This book introduces the applications, theory, and algorithms of linear and nonlinear optimization, with an emphasis on the practical aspects of the material. Its unique modular structure provides flexibility to accommodate the varying needs of instructors, students, and practitioners with different levels of sophistication in these topics. The succinct style of this second edition is punctuated with numerous real-life examples and exercises, and the authors include accessible explanations of topics that are not often mentioned in textbooks, such as duality in nonlinear optimization, primal-dual methods for nonlinear optimization, filter methods, and applications such as support vector machines.

• Coursebook for Graduate Students
• Publisher: SIAM
• Year: 2008