Series acceleration

Related lectures (32)

Covers Richardson extrapolation for numerical differentiation to reduce error.

Introduces Simpson's rule for numerical integration and Richardson extrapolation for accuracy improvement.

Explores strong convexity's role in faster convergence rates for optimization algorithms.

Presents a generalized optimistic framework for solving convex-concave saddle point problems, extending to higher-order methods.

Explores inference for stochastic differential equations, focusing on numerical methods and convergence analysis.

Explains verification and validation in CFD simulations, focusing on Richardson extrapolation and accuracy checking.

Explores Markov chain convergence, spectral gap, and acceleration techniques for faster convergence.

Explores the optimality of convergence rate in gradient descent and acceleration techniques for convex and non-convex problems.

By Quoc Tran-Dinh explores accelerated algorithms for monotone inclusions, covering optimization models, challenges, and new algorithms.

Discusses corrections to old videos on convergence methods and precision.

Explores error estimation in numerical integration and its applications in forecasting, emphasizing the Romberg method and Richardson extrapolation.

Explores the numerical analysis of nonlinear equations, focusing on convergence criteria and methods like bisection and fixed-point iteration.

Explains gradient descent with early stopping and stochastic gradient descent to optimize model training and prevent overfitting.

Explores Taylor series development, convergence criteria, and numerical applications.

Explores gradient descent with memory, momentum methods, conjugate gradients, and nonlinear CG on manifolds.

Explores numerical methods for solving the wave equation, discussing stability conditions and convergence rates.

Covers the convergence of Adaptive Langevin dynamics using hypocoercive techniques and explores the Central Limit Theorem.

Explores Newton's method convergence and quadratic convergence properties in theorem 8.4.

Explores accelerating the Value Iteration algorithm using control theory and matrix splitting techniques to achieve faster convergence.

Explains Lagrange interpolation for numerical integration and introduces Simpson's rules.

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