Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization

Prediction-Correction Interior-Point Method for Time-Varying Convex Optimization

In this paper, we develop an interior-point method for solving a class of convex optimization problems with timevarying objective and constraint functions. Using log-barrier penalty functions, we propose a continuous-time dynamical system for tracking the (time-varying) optimal solution with an asymptotically vanishing error. This dynamical system is composed of two terms: (i) a correction term...

Subspace Correction Methods for Convex Optimization

Linear Time Varying MPC Based Path Planning of an Autonomous Vehicle via Convex Optimization

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...

Decentralized Convex Optimization for Wireless Sensor Networks

Many real-world applications arising in domains such as large-scale machine learning, wired and wireless networks can be formulated as distributed linear least-squares over a large network. These problems often have their data naturally distributed. For instance applications such as seismic imaging, smart grid have the sensors geographically distributed and the current algorithms to analyze the...

Some New Methods for Prediction of Time Series by Wavelets

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...