Floating Point Numbers: LU Decomposition and Errors

Related lectures (28)

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

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

Explores the effect of rounding errors in solving linear systems using the LU factorization method.

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

Discusses the representation of real numbers in various bases and the implications of floating-point arithmetic.

Explores the Cholesky factorization method for symmetric positive definite matrices.

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

Covers derivation, integration, and simulation in Matlab, along with the representation of numbers and potential errors.

Explores direct methods for solving linear systems of equations, including Gauss elimination and LU decomposition.

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