Lecture
Convergence Analysis: Iterative Methods
In course
DEMO: velit aliquip eu ullamco
Sunt duis irure fugiat id. Nulla reprehenderit magna amet amet magna officia eiusmod laborum excepteur laboris nisi amet occaecat. Non amet ipsum culpa do laboris minim incididunt veniam cupidatat amet ad tempor elit. Nostrud tempor aute est laborum est mollit ut reprehenderit. Et quis mollit duis aliqua eu deserunt nisi consectetur eiusmod elit nostrud tempor voluptate.
Description
This lecture covers the convergence analysis of iterative methods, focusing on the spectral radius of a matrix and the conditions for convergence. It explains the iterative process, matrix iteration, and the spectral radius theorem. The lecture also discusses fixed point methods, gradient methods, and the importance of choosing the right parameters for convergence.
Instructors (2)
exercitation reprehenderit
Quis labore veniam elit amet duis nulla commodo et sunt non consequat ex duis. Tempor anim cupidatat id velit esse tempor fugiat fugiat veniam est esse aute. Laboris velit ut aliqua officia sunt non exercitation enim id. Deserunt aute mollit ad et laboris dolor Lorem pariatur laboris quis ipsum.
esse proident non minim
Nisi reprehenderit cillum aliquip eiusmod nostrud minim sint esse aliqua sint minim aute labore incididunt. Eiusmod elit pariatur consequat nulla fugiat qui quis cillum esse reprehenderit incididunt. Duis enim tempor culpa exercitation deserunt magna eiusmod cupidatat irure cillum nostrud. Minim exercitation ex fugiat pariatur labore dolor mollit non. Reprehenderit ullamco tempor quis id ut quis exercitation non deserunt amet voluptate nisi sunt adipisicing. Aute dolor pariatur nulla incididunt.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (58)
Numerical Analysis: Linear Systems
Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.
Matrix Diagonalization: Spectral Theorem
Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.
Richardson Method: Preconditioned Iterative Solvers
Covers the Richardson method for solving linear systems with preconditioned iterative solvers and introduces the gradient method.
Introduction to Quantum Chaos
Covers the introduction to Quantum Chaos, classical chaos, sensitivity to initial conditions, ergodicity, and Lyapunov exponents.
Iterative Methods: Linear Systems
Covers iterative methods for solving linear systems and discusses convergence criteria and spectral radius.