The solution of non-linear sets of algebraic equations is usually obtained by the Newton's method, exhibiting quadratic convergence. For practical simulations, a signiicant computational eeort consists in the evaluation of the Jacobian matrices. In this paper, we propose and experiment various methods to speed the convergence process either by re-using information from previous iterates or by b...

Interpolation-based model-checking and acceleration techniques have been widely proved successful and efficient for reachability checking. Surprisingly, these two techniques have never been combined to strengthen each other. Intuitively, acceleration provides under-approximation of the reachability set by computing the exact effect of some control-flow cycles and combining them with other trans...

A discussion of convergence acceleration techniques as they relate to computational fluid dynamics problems on unstructured meshes is given. Rather than providing a detailed description of particular methods, the various different building blocks of current solution techniques are discussed and examples of solution strategies using one or several of these ideas are given. Issues relating to uns...

The acceleration technique known as Iterated Defect Correction (IDeC) for the numerical solution of singular initial value problems is investigated. IDeC based on the implicit Euler method performs satisfactorily and can thus be used for the eÆcient solution of singular boundary value problems with the shooting method. Higher order one-step methods like the box scheme or the trapezoidal rule ca...

Proximal gradient descent (PGD) and stochastic proximal gradient descent (SPGD) are popular methods for solving regularized risk minimization problems in machine learning and statistics. In this paper, we propose and analyze an accelerated variant of these methods in the mini-batch setting. This method incorporates two acceleration techniques: one is Nesterov’s acceleration method, and the othe...

Typically, the linear image restoration problem is an ill-conditioned, underdetermined inverse problem. Here, stabilization is achieved via the introduction of a first-order smoothness constraint which allows the preservation of edges and leads to the minimization of a nonconvex functional. In order to carry through this optimization task, we use stochastic relaxation with annealing. We prefer ...