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Biconjugate gradient method
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Related lectures (30)
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Regression & Systemed Lineaires
Covers the principles of regression and linear systems, focusing on iterative methods.
Matrix Computations: Complexity and Algorithms
Explores matrix computation complexity, known algorithms, and structured solvers' impact.
Iterative Methods for Linear Equations
Explores iterative methods for linear equations, including Jacobi and Gauss-Seidel methods, convergence criteria, and the conjugate gradient method.
Conjugate Gradient Methods: Overview
Provides an overview of conjugate gradient methods, including preconditioning, nonlinear conjugate gradient, and singular value decomposition.
Iterative Methods for Linear Equations
Introduces iterative methods for solving linear equations and discusses the gradient method for minimizing errors.
Iterative Methods: Linear Systems
Explores iterative methods for solving linear systems, including Jacobi and Gauss-Seidel methods, Cholesky factorization, and preconditioned conjugate gradient.
Optimization Techniques: Gradient Method Overview
Discusses the gradient method for optimization, focusing on its application in machine learning and the conditions for convergence.
Convex Optimization: Gradient Algorithms
Covers convex optimization problems and gradient-based algorithms to find the global minimum.
Nonlinear Optimization
Covers line search, Newton's method, BFGS, and conjugate gradient in nonlinear optimization.
Preconditioned Richardson Method
Covers the Preconditioned Richardson Method for solving linear systems and the impact of preconditioning on convergence.
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