We study an infeasible interior path-following method for complemen-tarity problems. The method uses a wide neighborhood and takes one or two Newton steps per iteration. We show that the method attains global convergence, assuming the iterates are deened and bounded (which occurs when the function is a P 0-R 0-function or when the function is monotone and a suuciently positive solution exists)....

This paper proposes a component-based dual decomposition of the nonconvex AC optimal power flow (OPF) problem, where the modified dual function is solved in a distributed fashion. The main contribution of this work is that is demonstrates that a distributed method with carefully tuned parameters can converge to globally optimal solutions despite the inherent nonconvexity of the problem and the ...

As routing takes place in an entirely distributed system where local routers have no direct access to globally consistent network state information, a routing algorithm has to make uncertain forwarding decisions. As the network state may change, due to failures or new adoptions of networks, routing algorithms have to adapt themselves to the new situation. This network convergence phase should b...

Interlocutors are known to mutually adapt during conversation. Recent studies have questioned the adaptation of phonological representations and kinematics of phonetic variables such as loudness, speech rate or fundamental frequency. Results are often contradictory and the effectiveness of phonetic convergence during conversation is still an open issue. This paper describes an original experime...

The convergence property of a dynamical system is a strong condition with various useful implications. A convergent system exhibits a bounded globally attractively stable solution and thus its asymptotic (or symptotic) behaviour is independent of initial conditions. This paper presents conditions for the convergence property of mechanical systems submitted to unilateral constraints. A key role ...

Motivated by machine learning problems over large data sets and distributed optimization over networks, we develop and analyze a new method called incremental Newton method for minimizing the sum of a large number of strongly convex functions. We show that our method is globally convergent for a variable stepsize rule. We further show that under a gradient growth condition, convergence rate is ...

Quasi-Newton algorithms for unconstrained nonlinear minimization generate a sequence of matrices that can be considered as approximations of the objective function second derivatives. This paper gives conditions under which these approximations can be proved to converge globally to the true Hessian matrix, in the case where the Symmetric Rank One update formula is used. The rate of convergence ...

In a network of dynamical systems, concurrent synchronization is a regime where multiple groups of fully synchronized elements coexist. In the brain, concurrent synchronization may occur at several scales, with multiple "rhythms" interacting and functional assemblies combining neural oscillators of many different types. Mathematically, stable concurrent synchronization corresponds to convergenc...