831 research outputs found
Multi-Agent Distributed Optimization via Inexact Consensus ADMM
Multi-agent distributed consensus optimization problems arise in many signal
processing applications. Recently, the alternating direction method of
multipliers (ADMM) has been used for solving this family of problems. ADMM
based distributed optimization method is shown to have faster convergence rate
compared with classic methods based on consensus subgradient, but can be
computationally expensive, especially for problems with complicated structures
or large dimensions. In this paper, we propose low-complexity algorithms that
can reduce the overall computational cost of consensus ADMM by an order of
magnitude for certain large-scale problems. Central to the proposed algorithms
is the use of an inexact step for each ADMM update, which enables the agents to
perform cheap computation at each iteration. Our convergence analyses show that
the proposed methods converge well under some convexity assumptions. Numerical
results show that the proposed algorithms offer considerably lower
computational complexity than the standard ADMM based distributed optimization
methods.Comment: submitted to IEEE Trans. Signal Processing; Revised April 2014 and
August 201
Deep Extreme Multi-label Learning
Extreme multi-label learning (XML) or classification has been a practical and
important problem since the boom of big data. The main challenge lies in the
exponential label space which involves possible label sets especially
when the label dimension is huge, e.g., in millions for Wikipedia labels.
This paper is motivated to better explore the label space by originally
establishing an explicit label graph. In the meanwhile, deep learning has been
widely studied and used in various classification problems including
multi-label classification, however it has not been properly introduced to XML,
where the label space can be as large as in millions. In this paper, we propose
a practical deep embedding method for extreme multi-label classification, which
harvests the ideas of non-linear embedding and graph priors-based label space
modeling simultaneously. Extensive experiments on public datasets for XML show
that our method performs competitive against state-of-the-art result
Solving Multiple-Block Separable Convex Minimization Problems Using Two-Block Alternating Direction Method of Multipliers
In this paper, we consider solving multiple-block separable convex
minimization problems using alternating direction method of multipliers (ADMM).
Motivated by the fact that the existing convergence theory for ADMM is mostly
limited to the two-block case, we analyze in this paper, both theoretically and
numerically, a new strategy that first transforms a multi-block problem into an
equivalent two-block problem (either in the primal domain or in the dual
domain) and then solves it using the standard two-block ADMM. In particular, we
derive convergence results for this two-block ADMM approach to solve
multi-block separable convex minimization problems, including an improved
O(1/\epsilon) iteration complexity result. Moreover, we compare the numerical
efficiency of this approach with the standard multi-block ADMM on several
separable convex minimization problems which include basis pursuit, robust
principal component analysis and latent variable Gaussian graphical model
selection. The numerical results show that the multiple-block ADMM, although
lacks theoretical convergence guarantees, typically outperforms two-block
ADMMs
Quasiparticle interference of C2-symmetric surface states in LaOFeAs parent compound
We present scanning tunneling microscopy studies of the LaOFeAs parent
compound of iron pnictide superconductors. Topographic imaging reveals two
types of atomically flat surfaces, corresponding to the exposed LaO layer and
FeAs layer respectively. On one type of surface, we observe strong standing
wave patterns induced by quasiparticle interference of two-dimensional surface
states. The distribution of scattering wavevectors exhibits pronounced two-fold
symmetry, consistent with the nematic electronic structure found in the
Ca(Fe1-xCox)2As2 parent state.Comment: 13 pages, 4 figure
Outage Constrained Robust Secure Transmission for MISO Wiretap Channels
In this paper we consider the robust secure beamformer design for MISO
wiretap channels. Assume that the eavesdroppers' channels are only partially
available at the transmitter, we seek to maximize the secrecy rate under the
transmit power and secrecy rate outage probability constraint. The outage
probability constraint requires that the secrecy rate exceeds certain threshold
with high probability. Therefore including such constraint in the design
naturally ensures the desired robustness. Unfortunately, the presence of the
probabilistic constraints makes the problem non-convex and hence difficult to
solve. In this paper, we investigate the outage probability constrained secrecy
rate maximization problem using a novel two-step approach. Under a wide range
of uncertainty models, our developed algorithms can obtain high-quality
solutions, sometimes even exact global solutions, for the robust secure
beamformer design problem. Simulation results are presented to verify the
effectiveness and robustness of the proposed algorithms
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