Applied Linear Algebra for Research

A skill for applying linear algebra to research computing, data analysis, and scientific modeling. Covers matrix decompositions, eigenvalue problems, least squares, dimensionality reduction, and practical implementation in NumPy/SciPy.

Essential Operations

Matrix Multiplication and Solving Systems

import numpy as np
from scipy import linalg


def solve_linear_system(A: np.ndarray, b: np.ndarray) -> dict:
    """
    Solve Ax = b and analyze the system.

    Args:
        A: Coefficient matrix (n x n)