The field of computational methods for algebraic and linear problems is witnessing significant developments, with a focus on improving efficiency, accuracy, and scalability. Researchers are exploring innovative approaches to tackle complex problems, such as sparse polynomial multiplication, quaternion matrix inversion, and secure distributed matrix multiplication. These advances have the potential to impact various applications, including image processing, filtering, and deblurring. Noteworthy papers in this area include: Probably faster multiplication of sparse polynomials, which presents a probabilistic algorithm for efficient multiplication. Iterative Methods for Computing the Moore-Penrose Pseudoinverse of Quaternion Matrices, with Applications, which develops iterative methods for computing the Moore-Penrose pseudoinverse of quaternion matrices. Randomized Krylov methods for inverse problems, which proposes randomized Krylov subspace methods for efficiently computing regularized solutions to large-scale linear inverse problems.