We consider a Newton-CG augmented Lagrangian method for solving semidefinite programming (SDP) problems from the perspective of approximate semismooth Newton methods. In order to analyze the rate of convergence of our proposed method, we characterize the Lipschitz continuity of the corresponding solution mapping at the origin. For the inner problems, we show that the positive definiteness of th...

A time-implicit discretization is derived and validated for the material-point method (MPM). The resulting non-linear, discrete equations are solved using Newton’s method combined with either the conjugate gradient method or the generalized minimum residual method. These Newton-Krylov solvers are implemented in a matrix-free fashion for numerical efficiency. A description of the algorithms and ...

Resultants are defined in the sparse (or toric) context in order to exploit the structure of the polynomials as expressed by their Newton polytopes. Since determinantal formulae are not always possible, the most efficient general method for computing resultants is as the ratio of two determinants. This is made possible by Macaulay’s seminal result [15] in the dense homogeneous case, extended by...

We will present the Newton-Raphson algorithm, and the secant method. A MATLAB function for the Newton-Raphson method. The function.MATLAB has many tools that make this package well suited for numerical computations. The Newton-Raphson method for systems of nonlinear equations.The Newton-Raphson method, or Newton Method, is a powerful technique. Interesting is Section 3, where the birth of the N...

In this paper bounds for clusters of eigenvalues of non-selfadjoint matrices are investigated. We describe a method for the computation of rigorous error bounds for multiple or nearly multiple eigenvalues, and for a basis of the corresponding invariant subspaces. The input matrix may be real or complex, dense or sparse. The method is based on a quadratically convergent Newton-like method; it in...

This paper presents an algorithm for solving a class of large scale nonlinear programming problem which is originally derived from the multi-stage stochastic convex nonlinear programming. Using the Lagrangian-dual method and the Moreau-Yosida regularization, the primal problem is neatly transformed into a smooth convex problem. By introducing a self-concordant barrier function, an approximate g...