Newton Greedy Pursuit: A Quadratic Approximation Method for Sparsity-Constrained Optimization

First-order greedy selection algorithms have been widely applied to sparsity-constrained optimization. The main theme of this type of methods is to evaluate the function gradient in the previous iteration to update the non-zero entries and their values in the next iteration. In contrast, relatively less effort has been made to study the second-order greedy selection method additionally utilizing the Hessian information. Inspired by the classic constrained Newton method, we propose in this paper the NewTon Greedy Pursuit (NTGP) method to approximately minimizes a twice differentiable function over sparsity constraint. At each iteration, NTGP constructs a second-order Taylor expansion to approximate the cost function, and estimates the next iterate as the solution of the constructed quadratic model over sparsity constraint. Parameter estimation error and convergence property of NTGP are analyzed. The superiority of NTGP to several representative first-order greedy selection methods is demonstrated in synthetic and real sparse logistic regression tasks.