Explains Lagrange interpolation polynomials, a method for finding a polynomial that perfectly fits a given set of data points.
Explains the orthogonal Procrustes problem: finding a rotation matrix to align one matrix with another using SVD, with Python code.
A technical guide on building a block sparse matrix in C++ using the Eigen library, focusing on memory layout and performance.
A mathematical proof showing the determinant of a correlation matrix is at most 1, using eigenvalues and the AM-GM inequality.
Part 6 of a series on making sparse linear algebra differentiable in JAX, focusing on implementing Jacobian-vector products for custom primitives.
Part five of a series on implementing differentiable sparse linear algebra in JAX, focusing on registering new JAX-traceable primitives.
Explores using sparse linear algebra to speed up Bayesian inference for linear mixed models and generalizations, with a focus on Python/JAX prototyping.
Explains the mathematical derivation of generalized Fourier series expansions using orthogonal function bases and inner products.
Introduces linear algebra concepts like vector spaces and orthogonality as a foundation for understanding generalized Fourier series expansions.
Design discussion for new linear algebra data structures in QuTiP, focusing on lightweight vs. heavy implementations for performance and dispatch.
Explains efficient vectorized methods for sampling points from spline curves and 2-sphere splines using linear algebra and caching techniques.
A guide to implementing linear algebra concepts and matrix operations in JavaScript, using the math.js library for machine learning.
Explores computational challenges of large quadratic forms in genomics, focusing on eigenvalue approximations for high-dimensional statistical tests like SKAT.
Explains the Stochastic SVD algorithm, a probabilistic method for fast, approximate matrix decomposition using random projections.
A graduate reflects on their 5-year computer engineering master's at Linköping University, covering courses, projects, and personal growth.
A tutorial explaining Principal Component Analysis (PCA), a dimensionality reduction technique used in machine learning and data analysis.
A programmer analyzes whether math classes are useful in a software development career, based on personal experience.
Explores sparse Singular Value Decomposition (SVD) implementations in Python, comparing libraries like ARPACK, LAPACK, and PROPACK for computational efficiency.
A student reflects on their university courses in computer hardware, software prototyping, and linear algebra, connecting them to programming.
A Computer Science student reflects on finishing a Java game, enjoying courses like Data Structures, and reading tech/design books.
