Lectures related to Machine epsilon

Explores floating point numbers, LU decomposition, errors, and matrix properties.

Explores Runge Kutta and multistep methods for solving ODEs, including Backward Euler and Crank-Nicolson.

Explores floating point numbers, LU decomposition, errors in numerical computations, and the impact of round-off errors.

Introduces floating point number representation, round-off errors, and their impact on numerical computations.

Covers numerical differentiation, forward differences, Taylor's expansion, Big O notation, and error minimization.

Discusses numerical differentiation, error analysis, and finite difference formulas.

Explores direct methods for solving linear equations and the impact of errors on solutions and matrix properties.

Explores the Thomas algorithm for tridiagonal systems and the accuracy of direct methods in numerical computations.

Provides an overview of fixed-point and floating-point arithmetic in digital systems.

Covers the representation of real numbers in a computer using floating point representation and the dangers of significant digit cancellation.

Explores numerical differentiation using finite differences and addresses the impact of errors in computer computations.

Explores floating point numbers, their representation, precision, and associated errors.

Covers the practical aspects of using a Fehlmann type picomax machine, emphasizing tool handling, precision adjustments, and maintenance procedures.

Discusses numerical derivatives of order 1 and the effects of rounding errors on calculations.

Explores numerical differentiation methods and round-off errors in computer computations.

Explores linear systems, covering direct and iterative methods for solving them with a focus on round-off errors and Richardson's algorithm.

Covers high order ODEs, numerical methods, and stability criteria.

Explores LU factorization, rounding-off errors, sparse matrices, and matrix operations using MATLAB.