Mengdi Wang
Princeton University
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Publication
Featured researches published by Mengdi Wang.
IEEE Journal of Selected Topics in Signal Processing | 2015
Xiaohan Wang; Mengdi Wang; Yuantao Gu
The rapid development of signal processing on graphs provides a new perspective for processing large-scale data associated with irregular domains. In many practical applications, it is necessary to handle massive data sets through complex networks, in which most nodes have limited computing power. Designing efficient distributed algorithms is critical for this task. This paper focuses on the distributed reconstruction of a time-varying bandlimited graph signal based on observations sampled at a subset of selected nodes. A distributed least square reconstruction (DLSR) algorithm is proposed to recover the unknown signal iteratively, by allowing neighboring nodes to communicate with one another and make fast updates. DLSR uses a decay scheme to annihilate the out-of-band energy occurring in the reconstruction process, which is inevitably caused by the transmission delay in distributed systems. Proof of convergence and error bounds for DLSR are provided in this paper, suggesting that the algorithm is able to track time-varying graph signals and perfectly reconstruct time-invariant signals. The DLSR algorithm is numerically experimented with synthetic data and real-world sensor network data, which verifies its ability in tracking slowly time-varying graph signals.
Mathematical Programming | 2017
Mengdi Wang; Ethan X. Fang; Han Liu
Classical stochastic gradient methods are well suited for minimizing expected-value objective functions. However, they do not apply to the minimization of a nonlinear function involving expected values or a composition of two expected-value functions, i.e., the problem
Siam Journal on Optimization | 2016
Mengdi Wang; Dimitri P. Bertsekas
Mathematics of Operations Research | 2014
Mengdi Wang; Dimitri P. Bertsekas
\min _x \mathbf{E}_v\left[ f_v\big (\mathbf{E}_w [g_w(x)]\big ) \right] .
Mathematical Programming | 2018
Chris Junchi Li; Mengdi Wang; Han Liu; Tong Zhang
Stochastic Systems | 2013
Mengdi Wang; Dimitri P. Bertsekas
minxEvfv(Ew[gw(x)]). In order to solve this stochastic composition problem, we propose a class of stochastic compositional gradient descent (SCGD) algorithms that can be viewed as stochastic versions of quasi-gradient method. SCGD update the solutions based on noisy sample gradients of
international conference on acoustics, speech, and signal processing | 2015
Jialin Liu; Yuantao Gu; Mengdi Wang
international conference on acoustics, speech, and signal processing | 2014
Yuantao Gu; Mengdi Wang
f_v,g_{w}
Archive | 2012
Nick Polydorides; Mengdi Wang; Dimitri P. Bertsekas
Mathematical Programming | 2015
Mengdi Wang; Dimitri P. Bertsekas
fv,gw and use an auxiliary variable to track the unknown quantity