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Fast Linear Iterations for Distributed Averaging
Distributed consensus Linear system Spectral radius Graph Laplacian Semide!nite programming
2015/7/10
We consider the problem of finding a linear iteration that yields distributed averaging consensus over a network, i.e., that asymptotically computes the average of some initial values given at the nod...
Designing Fast Distributed Iterations via Semidefinite Programming
Designing Fas Distributed Iterations Semidefinite Programming
2015/7/10
The general setting we consider involves a process, iteration, or method in which the computation or communication at each step is local, determined by a given graph, and involves some parameters or c...
Fast Computation of Optimal Contact Forces
Convex optimization force closure friction cone grasp force interior-point method
2015/7/10
We consider the problem of computing the smallest contact forces, with point-contact friction model, that can hold an object in equilibrium against a known external applied force and torque. It is kno...
Fast Algorithms for Resource Allocation in Wireless Cellular Networks
Fast computation resource allocation scheduling wireless cellular networks
2015/7/9
We consider a scheduled orthogonal frequency division multiplexed (OFDM) wireless cellular network where the channels from the base-station to the n mobile users undergo flat fading. Spectral resource...
Fast Evaluation of Quadratic Control-Lyapunov Policy
Fast Evaluation Quadratic Control-Lyapunov Policy
2015/7/9
The evaluation of a control-Lyapunov policy, with quadratic Lyapunov function, requires the solution of a quadratic program (QP) at each time step. For small problems this QP can be solved explicitly;...
Metric Selection in Fast Dual Forward Backward Splitting
Metric Selection Fast Dual Forward Backward Splitting
2015/7/9
The performance of fast forward-backward splitting, or equivalently fast proximal gradient methods, is susceptible to conditioning of the optimization problem data. This conditioning is related to a m...
Monotonicity and Restart in Fast Gradient Methods
Monotonicity Restart Fast Gradient Methods
2015/7/9
Fast gradient methods are known to be non-monotone algorithms, and oscillations typically occur around the solution. To avoid this behavior, we propose in this paper a fast gradient method with restar...
First order optimization methods often perform poorly on ill-conditioned optimization problems. However, by preconditioning the problem data and solving the preconditioned problem, the performance of ...
LIFE IN THE FAST LANE:ORIGINS OF COMPETITIVE INTERACTION IN NEW VS. ESTABLISHED MARKETS
LIFE IN THE FAST LANE COMPETITIVE INTERACTION ESTABLISHED MARKETS
2015/7/3
Prior work examines competitive moves in relatively stable markets. In contrast, we focus on less stable markets where competitive advantages are temporary and R&D moves are essential. Using evolution...
Fast inference in generalized linear models via expected log-likelihoods
Fast inference generalized linear models expected log-likelihoods
2013/6/14
Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate comp...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
MCMC methods for Gaussian process models using fast approximations for the likelihood
MCMC methods for Gaussian process models using fast approximations for the likelihood
2013/6/14
Gaussian Process (GP) models are a powerful and flexible tool for non-parametric regression and classification. Computation for GP models is intensive, since computing the posterior density, $\pi$, fo...
Fast dimension-reduced climate model calibration
Fast dimension-reduced climate model calibration
2013/4/27
What is the response of the climate system to anthropogenic forcings? This question is addressed typically using projections from climate models. The uncertainty surrounding current climate projection...
A Fast Iterative Bayesian Inference Algorithm for Sparse Channel Estimation
A Fast Iterative Bayesian Inference Algorithm Sparse Channel Estimation
2013/4/27
In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited ...
Fast MCMC sampling for Markov jump processes and extensions
Markov jump process uniformization MCMC Gibbs sampler Markov-modulated Poisson process continuous-time Bayesian network
2012/9/17
Markov jump processes (or continuous-time Markov chains) are a simple and important class of continuous-time dynamical systems. In this paper, we tackle the problem of simu-lating from the posterior d...