Abstract The problem of sensor network localization (SNL) can be formulated as a semidefinite programming with rank constraint. We propose new method for solving such SNL problems. factorize matrix the constraint into product two matrices via Burer–Monteiro factorization. Then, we add difference matrices, penalty parameter, to objective function, thereby reformulating an unconstrained multiconv...

The steepest descent method for large linear systems is well-known to often converge very slowly, with the number of iterations required being about the same as that obtained by utilizing a gradient descent method with the best constant step size and growing proportionally to the condition number. Faster gradient descent methods must occasionally resort to significantly larger step sizes, which...

The steepest descent method proposed by Fliege and Svaiter has motivated the research on methods for multiobjective optimization, which received increasing attention in recent years. However, empirical results show that Armijo line search often a very small stepsize along direction, decelerates convergence seriously. This paper points out issue is mainly due to imbalances among objective functi...