I’ve been keeping track of AI since the early 1970s when I read a book, “The Sciences of the Artificial” by Herbert A. Simon, ...

An effective approach to solving sparse regularization problems is the iterative re-weighting least squares (IRLSs) algorithm. However, IRLS is computationally intensive and may not be suitable for ...

This paper investigates an agricultural planting strategy that integrates simulated annealing algorithm and linear programming. Addressing the crop planting issues in mountainous regions of Northern ...

This solver is adapted from the linear-fractional programming (LFP) from Mike Hohmeyer at UC Berkeley based on Raimund Seidel's algorithm. Kernel functions are reorganized.

Its batchability and distributability further enable scalable deployment in large-scale applications. Currently, MPAX supports linear programming (LP) and quadratic programming (QP), the foundational ...

W. Morven Gentleman, Algorithm AS 75: Basic Procedures for Large, Sparse or Weighted Linear Least Problems, Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 23, No. 3 ...