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Lecture
Linear Systems: Convergence and Methods
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Related lectures (27)
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Cache Memory
Explores cache memory design, hits, misses, and eviction policies in computer systems, emphasizing spatial and temporal locality.
Linear Systems: Iterative Methods
Covers iterative methods for solving linear systems, including Jacobi and Gauss-Seidel methods.
Eigenvalues and Eigenvectors
Explores eigenvalues, eigenvectors, and methods for solving linear systems with a focus on rounding errors and preconditioning matrices.
Linear Systems Resolution
Summarizes methods for resolving linear systems, including Gaussian elimination and LU decomposition.
Numerical Analysis: Linear Systems
Covers the analysis of linear systems, focusing on methods such as Jacobi and Richardson for solving linear equations.
Construction of an Iterative Method
Covers the construction of an iterative method for linear systems, emphasizing matrix decomposition and convexity.
Linear Systems: Iterative Methods
Explores linear systems and iterative methods like gradient descent and conjugate gradient for efficient solutions.
Gradient Descent
Covers the concept of gradient descent in scalar cases, focusing on finding the minimum of a function by iteratively moving in the direction of the negative gradient.
Untitled
Primal-dual Optimization: Extra-Gradient Method
Explores the Extra-Gradient method for Primal-dual optimization, covering nonconvex-concave problems, convergence rates, and practical performance.
