A Three-term Conjugate Gradient Method with a Random Parameter for Large-scale Unconstrained Optimization and its Application in Regression Model

In this paper, a new three-term conjugate gradient algorithm is proposed to solve unconstrained optimization including regression problems. We minimize the distance between search direction matrix and self-scaling memoryless BFGS in Frobenius norm determine direction, which has same advantages as quasi-Newton method. At time, random parameter used so that satisfies sufficient descent condition. For uniformly convex functions general nonlinear functions, we all establish global convergence of Numerical experiments show our method nice numerical performance for solving large-scale optimization. addition, application model proves effective.