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Results 1 - 10
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71
Convex and network flow optimization for structured sparsity
- JMLR
, 2011
"... We consider a class of learning problems regularized by a structured sparsity-inducing norm defined as the sum of ℓ2- or ℓ∞-norms over groups of variables. Whereas much effort has been put in developing fast optimization techniques when the groups are disjoint or embedded in a hierarchy, we address ..."
Abstract
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Cited by 35 (8 self)
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We consider a class of learning problems regularized by a structured sparsity-inducing norm defined as the sum of ℓ2- or ℓ∞-norms over groups of variables. Whereas much effort has been put in developing fast optimization techniques when the groups are disjoint or embedded in a hierarchy, we address
A characterization of convex problems in decentralized control
- IEEE Transactions on Automatic Control
"... Abstract—We consider the problem of constructing optimal decentralized controllers. We formulate this problem as one of minimizing the closed-loop norm of a feedback system subject to constraints on the controller structure. We define the notion of quadratic invariance of a constraint set with respe ..."
Abstract
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Cited by 133 (24 self)
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test for sparsity constraints to be quadratically invariant, and thus amenable to convex synthesis. Index Terms—Convex optimization, decentralized control, de-layed control, extended linear spaces, networked control. I.
On the Convexity of Latent Social Network Inference
"... In many real-world scenarios, it is nearly impossible to collect explicit social network data. In such cases, whole networks must be inferred from underlying observations. Here, we formulate the problem of inferring latent social networks based on network diffusion or disease propagation data. We co ..."
Abstract
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Cited by 55 (4 self)
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approach based on convex programming with a l1-like penalty term that encourages sparsity. Experiments on real and synthetic data reveal that our method near-perfectly recovers the underlying network structure as well as the parameters of the contagion propagation model. Moreover, our approach scales well
Simultaneous Routing and Resource Allocation via Dual Decomposition
, 2004
"... In wireless data networks the optimal routing of data depends on the link capacities which, in turn, are determined by the allocation of communications resources (such as transmit powers and bandwidths) to the links. The optimal performance of the network can only be achieved by simultaneous optimi ..."
Abstract
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Cited by 171 (7 self)
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optimization of routing and resource allocation. In this paper, we formulate the simultaneous routing and resource allocation problem and exploit problem structure to derive ef£cient solution methods. We use a capacitated multicommodity flow model to describe the data ¤ows in the network. We assume
Solving The Convex Cost Integer Dual Network Flow Problem
- MANAGEMENT SCIENCE
, 1999
"... In this paper, we consider an integer convex optimization problem where the objective function is the sum of separable convex functions (that is, of the form (i,j)Q ij ij F(w)+ iP ii B( ) ), the constraints are similar to those arising in the dual of a minimum cost flow problem (that is, of the f ..."
Abstract
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Cited by 42 (5 self)
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relaxation technique, the convex cost integer dual network flow problem can be transformed to a convex cost primal network flow problem where each cost function is a piecewise linear convex function with integer slopes. Its special structure allows the convex cost primal network flow problem to be solved
Unveiling anomalies in large-scale networks via sparsity and low rank
- in Proceedings of 45th Asilomar Conference on Signals, Systems, and Computers
, 2011
"... Abstract—In the backbone of large-scale networks, traffic flows experience abrupt unusual changes which can result in congestion, and limit the extent to which end-user quality of service requirements are met. Diagnosing such traffic volume anomalies is a crucial task towards engineering the traffic ..."
Abstract
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Cited by 2 (1 self)
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the traffic in the network. This is challenging however, since the available data are the superposition of unobservable origin-to-destination (OD) flows per link. Leveraging the low intrinsic-dimensionality of OD flows and the sparse nature of anomalies, a convex program is for-mulated to unveil anomalies
1Convex Relaxation for Optimal Power Flow Problem: Mesh Networks
"... Abstract—This paper is concerned with a fundamental re-source allocation problem for electrical power networks. This problem, named optimal power flow (OPF), is nonconvex due to the nonlinearities imposed by the laws of physics, and has been studied since 1962. We have recently shown that a convex r ..."
Abstract
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Abstract—This paper is concerned with a fundamental re-source allocation problem for electrical power networks. This problem, named optimal power flow (OPF), is nonconvex due to the nonlinearities imposed by the laws of physics, and has been studied since 1962. We have recently shown that a convex
CORRELATION-AWARE SPARSITY-ENFORCING SENSOR PLACEMENT FOR SPATIO-TEMPORAL FIELD ESTIMATION
"... In this work, we propose a generalized framework for designing op-timal sensor constellations for spatio-temporally correlated field es-timation using wireless sensor networks. The accuracy of the field intensity estimate in every point of a given service area strongly de-pends upon the number and t ..."
Abstract
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Cited by 1 (1 self)
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-separable space-time covariance structure of the field. Index Terms — Wireless sensor network, field estimation, Bayesian framework, convex optimization, sparsity. 1.
Low-Rank Solution of Convex Relaxation for Optimal Power Flow Problem
"... Abstract—This paper is concerned with solving the nonconvex problem of optimal power flow (OPF) via a convex relaxation based on semidefinite programming (SDP). We have recently shown that the SDP relaxation has a rank-1 solution from which the global solution of OPF can be found, provided the power ..."
Abstract
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Cited by 1 (1 self)
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Abstract—This paper is concerned with solving the nonconvex problem of optimal power flow (OPF) via a convex relaxation based on semidefinite programming (SDP). We have recently shown that the SDP relaxation has a rank-1 solution from which the global solution of OPF can be found, provided
Lagrangian heuristics for strictly convex quadratic minimum cost network flow problems
, 2005
"... This thesis presents a study of five different Lagrangian heuristics applied to the strictly convex quadratic minimum cost network flow problem. Tests are conducted on randomly generated transportation networks with different degrees of sparsity and nonlinearity according to a system devised by Ohuc ..."
Abstract
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Cited by 1 (1 self)
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This thesis presents a study of five different Lagrangian heuristics applied to the strictly convex quadratic minimum cost network flow problem. Tests are conducted on randomly generated transportation networks with different degrees of sparsity and nonlinearity according to a system devised
Results 1 - 10
of
71
