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Large Scale Density-friendly Graph Decomposition via Convex Programming
2017
Proceedings of the 26th International Conference on World Wide Web - WWW '17
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Unfortunately, their algorithm for computing the exact decomposition is based on a maximum-flow algorithm which cannot scale to massive graphs, while the approximate decomposition defined by the same authors ...
that its components are arranged in order of their densities. ...
Convex Program. ...
doi:10.1145/3038912.3052619
dblp:conf/www/DanischCS17
fatcat:wnbxddby3nbkfarre5bxrujw3e
Large-Scale Convex Optimization for Dense Wireless Cooperative Networks
2015
IEEE Transactions on Signal Processing
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2015 pre-print arXiv:1506.00749v1 fatcat:inwmguyibnfujcgcqmqlcnmxby
In the proposed approach, the original large-scale convex problem is transformed into a standard cone programming form in the first stage via matrix stuffing, which only needs to copy the problem parameters ...
In this paper, we present a novel two-stage approach to solve large-scale convex optimization problems for dense wireless cooperative networks, which can effectively detect infeasibility and enjoy modeling ...
In summary, the proposed two-stage based large-scale convex optimization framework scales well to large-scale problem modeling and solving simultaneously. ...
doi:10.1109/tsp.2015.2443731
fatcat:tnwwmheyyzcsneodde2y3ai6pm
The Tightness of Multipartite Coherence from Spectrum Estimation
2021
Entropy
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Detecting multipartite quantum coherence usually requires quantum state reconstruction, which is quite inefficient for large-scale quantum systems. ...
we investigate the tightness of the estimated lower bound of various coherence measures, including the geometric measure of coherence, the l1-norm of coherence, the robustness of coherence, and some convex ...
However, detecting or estimating most coherence measures requires the reconstruction of quantum states, which is inefficient for large-scale quantum systems. ...
doi:10.3390/e23111519
pmid:34828217
pmcid:PMC8621860
fatcat:ihne5ddylrgxrjhdilsjhlcxxi
Singular Value Thresholding Algorithm for Wireless Sensor Network Localization
2020
Mathematics
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Common way to tackle this problem is by implementing the Semidefinite Programming (SDP). However, SDP can only handle matrices with a size of less than 100 × 100. ...
This algorithm is a user-friendly algorithm which produces a low computational cost with low storage capacity required to give the lowest-rank minimum nuclear norm solution. ...
This method is known as a convex function, which can be optimized simply via semidefinite programming (SDP) [14] . ...
doi:10.3390/math8030437
fatcat:bw2x6vjbwffdlktmwgtgcakenm
Scalable Computation of Regularized Precision Matrices via Stochastic Optimization
[article]
2015
arXiv
pre-print
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The associated convex semidefinite optimization problem is notoriously difficult to scale to large problems and has demanded significant attention over the past several years. ...
large-scale problems of contemporary interest. ...
Eigen decompositions and matrix inversions are less memory friendly, when compared to Cholesky decompositions for large problem sizes. ...
arXiv:1509.00426v1
fatcat:nwlyl2oiyzhsbmeyeit6bkopbm
Experimental Multi-Qubit Robustness by Local Encoding
[article]
2019
arXiv
pre-print
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The first generation of many-body quantum technologies will consist of noisy, intermediate-scale devices for which active error correction remains out of reach. ...
Here, we experimentally demonstrate that single-qubit encoding can significantly enhance the robustness of entanglement and coherence of four-qubit graph states against local noise with a preferred axis ...
We expect it to be a useful tool for large-scale quantum information devices in realistic, noisy conditions. ...
arXiv:1903.08667v1
fatcat:afenhixlzfekdieukatakcuo3i
Computational Statistics and Data Science in the Twenty-first Century
[article]
2022
arXiv
pre-print
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As the scale and complexity of scientific and industrial data grow, the discipline of computational statistics assumes an increasingly central role among the statistical sciences. ...
The log-likelihood (or log-density in the Bayesian setting) is one such quantity, and one can employ the computational graph framework (Lunn et al., 2009; Bergstra et al., 2010) to evaluate conditional ...
convexity. ...
arXiv:2204.05530v1
fatcat:a56l5ng4uzcsjn7nhnq7eimhhy
Learning socially normative robot navigation behaviors with Bayesian inverse reinforcement learning
2016
2016 IEEE International Conference on Robotics and Automation (ICRA)
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In the past, such approaches have been typically used to learn only simple behaviors under relatively controlled conditions using rigid representations or with methods that scale poorly to large domains ...
In experiments with a real robot and a large-scale pedestrian simulator, we are able to show that the approach enables a robot to learn complex navigation behaviors of varying degrees of social normativeness ...
on sets of sampled trajectories and is applicable to large scale domains. ...
doi:10.1109/icra.2016.7487452
dblp:conf/icra/OkalA16
fatcat:yp6veapukfgd3bcrre5xsphre4
The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation
[article]
2021
arXiv
pre-print
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This divergence works hand in hand with the entropic regularization approach which is popular to solve large scale optimal transport problems. ...
We show that the underlying non-convex optimization problem can be efficiently tackled using a highly parallelizable and GPU-friendly iterative scheme. ...
Peyré was supported in part by the French government under management of Agence Nationale de la Recherche as part of the "Investissements d'avenir" program, reference ANR19-P3IA-0001 (PRAIRIE 3IA Institute ...
arXiv:2009.04266v2
fatcat:zjcsmwlxxzeufnkv3j43kuanjq
JuMP: A Modeling Language for Mathematical Optimization
2017
SIAM Review
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JuMP takes advantage of advanced features of the Julia programming language to offer unique functionality while achieving performance on par with commercial modeling tools for standard tasks. ...
CVX, based on the principle of disciplined convex programming (DCP) [40] , allows users to express convex optimization problems in a specialized format which can be transformed into or approximated by ...
As suggested by its performance and the omission of Hessian computations, YALMIP's derivativebased nonlinear functionality is seemingly not designed for large-scale problems. ...
doi:10.1137/15m1020575
fatcat:fefgcfe7x5apjgu7ldi6ph5hfu
Implementation of Fog computing for reliable E-health applications
2015
2015 49th Asilomar Conference on Signals, Systems and Computers
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The non-convex rate maximization is approached via the corresponding mean-squared error minimization problem, which is further approximated to formulate a sequence of semidefinite programs. ...
The alternating directions method of multipliers (ADMM) is an optimization technique that can be used to solve large-scale factorable convex problem in an efficient distributed manner. ...
doi:10.1109/acssc.2015.7421170
dblp:conf/acssc/CraciunescuMMKP15
fatcat:qm6mki5z6bcvrfimkmqjyrxaxm
The core decomposition of networks: theory, algorithms and applications
2019
The VLDB journal
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[38] proposed a scalable algorithm for computing such a decomposition, based on convex programming. ...
Based on this observation, Tatti and Gionis [141] introduced the concept of density-friendly graph decomposition, where i) the density of the inner core subgraphs given by the decomposition is higher ...
doi:10.1007/s00778-019-00587-4
fatcat:pqjbqxixvzfijijfmr57ht4uiy
Crossing versus locking: Bit threads and continuum multiflows
[article]
2020
arXiv
pre-print
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Subject to a density bound, the maximum number of threads connecting a boundary region to its complement computes the Ryu-Takayanagi entropy. ...
When considering several regions at the same time, for example in proving entropy inequalities, there are various inequivalent density bounds that can be imposed. ...
(Here "small" means small compared to the curvature scales of M but large compared to the Planck scale.) ...
arXiv:2008.03197v1
fatcat:7ci7qlpkmbb6fokiuwboe77hmy
Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 15361)
2016
Dagstuhl Reports
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We will also discuss certain extensions of TORRENT that can scale efficiently to large scale problems, such as high dimensional sparse recovery. ...
Eldan, "Multi-scale exploration of convex functions and bandit convex optimization," arXiv:1507.06580v1 [math.MG], 2015. ...
doi:10.4230/dagrep.5.8.54
dblp:journals/dagstuhl-reports/0001LR15
fatcat:u63lnb5j4ba3fdgvyes2u3woji
Low-Rank Matrix Completion: A Contemporary Survey
2019
IEEE Access
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We also discuss the convolutional neural network (CNN)-based LRMC algorithms exploiting the graph structure of a low-rank matrix. ...
is an eigen-decomposition of the graph Laplacian R r [63] . ...
In such large-scale problems, algorithms such as LMaFit and LRGeomCG might be a good option since their computational complexity scales linearly with the number of observed entries O(r| |) while the complexity ...
doi:10.1109/access.2019.2928130
fatcat:6mlyzvb5szfvbic6qzciihtolu
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