In this paper a new method is introduced for path planning of an autonomous vehicle. In this method, the environment is considered cluttered and with some uncertainty sources. Thus, the state of detected object should be estimated using an optimal filter. To do so, the state distribution is assumed Gaussian. Thus the state vector is estimated by a Kalman filter at each time step. The estimation...

In this paper, we focus on the solution of online optimization problems that arise often in signal processing and machine learning, which have access to streaming sources data. We discuss algorithms for based prediction-correction paradigm, both primal dual space. particular, leverage typical regularized least-squares structure appearing many propose a novel tailored prediction strategy, call e...

Stochastic networks are a plausible representation of the relational information among entities in dynamic systems such as living cells or social communities. While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done toward estimating time-varying networks from time series of entity attributes. In this paper we present tw...

Inspired by classical sensitivity results for nonlinear optimization, we derive and discuss new quantitative bounds to characterize the solution map dual variables of a parametrized program. In particular, explicit expressions local global Lipschitz constants nonconvex or convex optimization problems, respectively. Our are geared towards study time-varying which commonplace in various applicati...

We study the problem of decentralized optimization with strongly convex smooth cost functions. This paper investigates accelerated algorithms under time-varying network constraints. In our approach, nodes run a multi-step gossip procedure after taking each gradient update, thus ensuring approximate consensus at iteration. The outer cycle is based on Nesterov scheme. Both computation and communi...