Distributed Stochastic Optimization over Time-Varying Noisy Network

This paper is concerned with distributed stochastic multi-agent optimization problem over a class of time-varying network with slowly decreasing communication noise effects. This paper considers the problem in composite optimization setting which is more general in noisy network optimization. It is noteworthy that existing methods for noisy network optimization are Euclidean projection based. We present two related different classes of non-Euclidean methods and investigate their convergence behavior. One is distributed stochastic composite mirror descent type method (DSCMD-N) which provides a more general algorithm framework than former works in this literature. As a counterpart, we also consider a composite dual averaging type method (DSCDA-N) for noisy network optimization. Some main error bounds for DSCMD-N and DSCDA-N are obtained. The trade-off among stepsizes, noise decreasing rates, convergence rates of algorithm is analyzed in detail. To the best of our knowledge, this is the first work to analyze and derive convergence rates of optimization algorithm in noisy network optimization. We show that an optimal rate of $O(1/\sqrt{T})$ in nonsmooth convex optimization can be obtained for proposed methods under appropriate communication noise condition. Moveover, convergence rates in different orders are comprehensively derived in both expectation convergence and high probability convergence sense.Comment: 27 page

Approximate Dual Averaging Method for Multiagent Saddle-Point Problems with Stochastic Subgradients

This paper considers the problem of solving the saddle-point problem over a network, which consists of multiple interacting agents. The global objective function of the problem is a combination of local convex-concave functions, each of which is only available to one agent. Our main focus is on the case where the projection steps are calculated approximately and the subgradients are corrupted by some stochastic noises. We propose an approximate version of the standard dual averaging method and show that the standard convergence rate is preserved, provided that the projection errors decrease at some appropriate rate and the noises are zero-mean and have bounded variance

Improved Dynamic Regret of Distributed Online Multiple Frank-Wolfe Convex Optimization

In this paper, we consider a distributed online convex optimization problem over a time-varying multi-agent network. The goal of this network is to minimize a global loss function through local computation and communication with neighbors. To effectively handle the optimization problem with a high-dimensional and structural constraint set, we develop a distributed online multiple Frank-Wolfe algorithm to avoid the expensive computational cost of projection operation. The dynamic regret bounds are established as $\mathcal{O}(T^{1-\gamma}+H_T)$ with the linear oracle number $\mathcal{O} (T^{1+\gamma})$, which depends on the horizon (total iteration number) $T$, the function variation $H_T$, and the tuning parameter $0<\gamma<1$. In particular, when the prior knowledge of $H_T$ and $T$ is available, the bound can be enhanced to $\mathcal{O} (1+H_T)$. Moreover, we illustrate the significant advantages of the multiple iteration technique and reveal a trade-off between dynamic regret bound, computational cost, and communication cost. Finally, the performance of our algorithm is verified and compared through the distributed online ridge regression problems with two constraint sets

Distributed Solvers for Network Linear Equations with Scalarized Compression

In this paper, we study distributed solvers for network linear equations over a network with node-to-node communication messages compressed as scalar values. Our key idea lies in a dimension compression scheme including a dimension compressing vector that applies to individual node states to generate a real-valued message for node communication as an inner product, and a data unfolding step in the local computations where the scalar message is plotted along the subspace generated by the compression vector. We first present a compressed average consensus flow that relies only on such scalar communication, and show that exponential convergence can be achieved with well excited signals for the compression vector. We then employ such a compressed consensus flow as a fundamental consensus subroutine to develop distributed continuous-time and discrete-time solvers for network linear equations, and prove their exponential convergence properties under scalar node communications. With scalar communications, a direct benefit would be the reduced node-to-node communication channel capacity requirement for distributed computing. Numerical examples are presented to illustrate the effectiveness of the established theoretical results.Comment: 8 pages, 4 figure

Atmospheric circulation of hot Jupiters: Coupled radiative-dynamical general circulation model simulations of HD 189733b and HD 209458b

We present global, three-dimensional numerical simulations of HD 189733b and HD 209458b that couple the atmospheric dynamics to a realistic representation of non-gray cloud-free radiative transfer. The model, which we call the Substellar and Planetary Atmospheric Radiation and Circulation (SPARC) model, adopts the MITgcm for the dynamics and uses the radiative model of McKay, Marley, Fortney, and collaborators for the radiation. Like earlier work with simplified forcing, our simulations develop a broad eastward equatorial jet, mean westward flow at higher latitudes, and substantial flow over the poles at low pressure. For HD 189733b, our simulations without TiO and VO opacity can explain the broad features of the observed 8 and 24-micron light curves, including the modest day-night flux variation and the fact that the planet/star flux ratio peaks before the secondary eclipse. Our simulations also provide reasonable matches to the Spitzer secondary-eclipse depths at 4.5, 5.8, 8, 16, and 24 microns and the groundbased upper limit at 2.2 microns. However, we substantially underpredict the 3.6-micron secondary-eclipse depth, suggesting that our simulations are too cold in the 0.1-1 bar region. Predicted temporal variability in secondary-eclipse depths is ~1% at Spitzer bandpasses, consistent with recent observational upper limits at 8 microns. We also show that nonsynchronous rotation can significantly alter the jet structure. For HD 209458b, we include TiO and VO opacity; these simulations develop a hot (>2000 K) dayside stratosphere. Despite this stratosphere, we do not reproduce current Spitzer photometry of this planet. Light curves in Spitzer bandpasses show modest phase variation and satisfy the observational upper limit on day-night phase variation at 8 microns. (abridged)Comment: 20 pages (emulate-apj format), 21 figures, final version now published in ApJ. Includes expanded discussion of radiative-transfer methods and two new figure