The field of optimization and numerical analysis is witnessing significant developments, with a focus on improving the efficiency and accuracy of algorithms. Researchers are exploring new methods to enhance the performance of optimization techniques, such as incorporating curvature information and developing stochastic algorithms. Additionally, there is a growing interest in developing scalable solutions for high-dimensional problems, including novel approaches to hyperinterpolation and quadrature rules. Noteworthy papers include: A Stochastic Algorithm for Searching Saddle Points with Convergence Guarantee, which proposes a stochastic saddle-search algorithm with a convergence guarantee. How many integrals should be evaluated at least in two-dimensional hyperinterpolation, which introduces a novel approach to approximating continuous functions over high-dimensional hypercubes by integrating matrix CUR decomposition with hyperinterpolation techniques.