A MATLAB project comparing the performance (time, iterations) of from-scratch implementations of direct and iterative methods (Gaussian Elimination, Gauss-Jordan Elimination, LU, Jacobi, Gauss-Seidel, ...

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Standard computer implementations of Dantzig's simplex method for linear programming are based upon forming the inverse of the basic matrix and updating the inverse ...

ABSTRACT: Continuously differentiable radial basis functions (C∞-RBFs), while being theoretically exponentially convergent are considered impractical computationally because the coefficient matrices ...

Abstract: This paper presents high-performance batched lower-upper (LU) factorization routines for small matrices on graphics processing units (GPUs). LU factorization is an effective method for ...

2022 IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms (IA3) Decomposing sparse matrices into lower and upper triangular matrices (sparse LU factorization) is a key operation ...

Figure 1. Basic workflow for NA-MEMD Causal Decomposition.

Linear Equations solver project done using Matlab, uses different method to solve the equations as Gauss Elimination, Gauss Jordan, LU Decomposition, Gauss Seidel, and Jacobi Iterative Method ...

ABSTRACT: Nonnegative matrix factorization (NMF) is a relatively new unsupervised learning algorithm that decomposes a nonnegative data matrix into a parts-based, lower dimensional, linear ...