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Deterministic consensus maximization with biconvex programming
[article]
2018
arXiv
pre-print
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In this paper, we propose an efficient deterministic optimization algorithm for consensus maximization. ...
We show how each iteration of the update can be formulated as an instance of biconvex programming, which we solve efficiently using a novel biconvex optimization algorithm. ...
Conclusions We proposed a novel deterministic algorithm for consensus maximization with non-linear residuals. ...
arXiv:1807.09436v3
fatcat:75vsx652ofgaxnopmqehoy5kym
Deterministic Consensus Maximization with Biconvex Programming
[chapter]
2018
Lecture Notes in Computer Science
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In this paper, we propose an efficient deterministic optimization algorithm for consensus maximization. ...
We show how each iteration of the update can be formulated as an instance of biconvex programming, which we solve efficiently using a novel biconvex optimization algorithm. ...
Conclusions We proposed a novel deterministic algorithm for consensus maximization with non-linear residuals. ...
doi:10.1007/978-3-030-01258-8_42
fatcat:u26mjbzoj5crjbbkpoovt72oji
Simultaneous Consensus Maximization and Model Fitting
[article]
2020
arXiv
pre-print
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This work proposes a new formulation to achieve simultaneous maximum consensus and model estimation (MCME), which has two significant features compared with traditional MC robust fitting. ...
Typically, it firstly finds a consensus set of inliers and then fits a model on the consensus set. ...
Simultaneous Consensus Maximization and Model Fitting Fei Wen, Hewen Wei, Yipeng Liu, and Peilin Liu Abstract-Maximum consensus (MC) robust fitting is a fundamental problem in low-level vision to process ...
arXiv:2008.01574v1
fatcat:n6bmdn5gcbbffo6jcr5ipbucxi
Optimized Transmission for Parameter Estimation in Wireless Sensor Networks
[article]
2019
arXiv
pre-print
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Along with formulating the design problem for a fusion center, we further present a consensus-based framework for decentralized estimation of deterministic parameters in a distributed network, which results ...
The numerical results confirm the computational advantage of the suggested approach in comparison with the state-of-the-art methods---an advantage that becomes more pronounced when the sensor network grows ...
Remark 2: When Θ represents the finite-energy constraint, problem (33) is biconvex in (y, a), and (40) is simply a quadratically constrained quadratic program (QCQP). ...
arXiv:1908.00600v1
fatcat:vxgpfizp7zdtpnyv3huss6usu4
Proportional Fair Resource Allocation on an Energy Harvesting Downlink - Part II: Algorithms
[article]
2012
arXiv
pre-print
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The goal is to solve an optimization problem designed to maximize a throughput-based utility function that provides proportional fairness among users. ...
INTRODUCTION With increasing awareness of the potential harmful effects to the environment caused by "greenhouse gas" emissions and the depletion of non-renewable energy sources, there is a growing consensus ...
This is a constrained optimization problem that aims to maximize the utility function with respect to the time and energy constraints. ...
arXiv:1205.5153v1
fatcat:lnqbxjekebb3flanrmy6c6sq5y
Parallel clustering algorithms
1989
Parallel Computing
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Then each cluster is further processed with an assembly tool for generating one consensus sequence per putative transcript. ...
Since the parallel program modelled with TIG graph does not capture the execution dependencies between tasks, the TIG model is more suitable for more complex parallel programs. ...
doi:10.1016/0167-8191(89)90036-7
fatcat:uly53om5cjgzvcfdft5mkikafe
An Efficient Solution to Non-Minimal Case Essential Matrix Estimation
[article]
2020
arXiv
pre-print
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To deal with outliers, we propose a robust N-point method using M-estimators. ...
First we formulate the problem as a quadratically constrained quadratic program (QCQP). Then a certifiably globally optimal solution to this problem is obtained by semidefinite relaxation. ...
Inlier set maximization can be achieved by randomized sampling methods [5] , [7] or deterministic optimization methods [15] , [16] . ...
arXiv:1903.09067v3
fatcat:i7qkkc74pzcivdcnswftdvblkm
Parallel and Distributed Successive Convex Approximation Methods for Big-Data Optimization
[article]
2018
arXiv
pre-print
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Example 6 (The Expectation-Maximization algorithm). ...
The first two rules above are deterministic rules. ...
. , I; and by with B 1 being a finite positive constant, it follows that consensus is asymptotically reached if lim k→∞ x k (i) −x k φ = 0, for all i = 1, . . . , I. ...
arXiv:1805.06963v1
fatcat:fbjziifyezdixoudqgi2sbfpum
On the Reconstruction Risk of Convolutional Sparse Dictionary Learning
[article]
2018
arXiv
pre-print
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Finally, we verify our theoretical results with numerical experiments on synthetic data. ...
The work was also partly supported by NSF IIS-1563887, Darpa D3M program, and AFRL (to B.P.), and NSF IIS-1717205 and NIH HG007352 (to J.M.). ...
Related Work There has been some work theoretically analyzing the nonconvex optimization problem (2) in terms of which IID SDL is typically cast [30, 47, 46] , with a consensus that despite being non-convex ...
arXiv:1708.08587v2
fatcat:galyqn5iurhjzibkqmrrfwozwa
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1808.05403v2 fatcat:wxjzwd67cvc2jig5feczrlmuqaOther Versions
Deterministic algorithms for the maximum consensus 0 minimization have been proposed recently in [217] , [230] , which showed superior performance in both solution quality and efficiency. ...
For fixed d i ≥ 0, minimizing the objective in (46) is equivalent to maximizing s T i Me i with respect to s i and e i . ...
arXiv:1808.05403v3
fatcat:lfq3t5gvgngmllu27ml7xnehtm
Monotone Boolean Functions, Feasibility/Infeasibility, LP-type problems and MaxCon
[article]
2020
arXiv
pre-print
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We illustrate, with examples from Computer Vision, how the resulting perspectives suggest new algorithms. ...
This paper outlines connections between Monotone Boolean Functions, LP-Type problems and the Maximum Consensus Problem. ...
https://github.com/ZhipengCai/MaxConTreeSearch 6 https://github.com/ZhipengCai/Demo---Deterministic-consensus-\maximization-with-biconvex-programming ...
arXiv:2005.05490v1
fatcat:awj77xoqyrgste5yrsrgjscqdq
Blind Identification of Graph Filters
2017
IEEE Transactions on Signal Processing
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This paper deals with the problem of joint identification of a graph filter and its input signal, thus broadening the scope of classical blind deconvolution of temporal and spatial signals to the less-structured ...
the biconvex compressed sensing approach in [13] . ...
,N } |u li | 2 = Sρ U (1) (19) where the first equality follows from the fact that maximizing over all S-subsets first and then maximizing over a particular entry i ∈ Ω is equivalent to an initial maximization ...
doi:10.1109/tsp.2016.2628343
fatcat:zyo5ge2njveutdsl7d7hmnrzom
XXIInd Congress of the European Society for Stereotactic and Functional Neurosurgery. Madrid, Spain,September 28-October 1, 2016: Abstracts
2016
Stereotactic and Functional Neurosurgery
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Methods: We retrospectively analyzed 86 trajectories of DBS electrodes implanted into the subthalamic nucleus (STN) of patients with advanced Parkinson's disease. ...
Discussion: Our initial results demonstrate that we can successfully record local field potentials, detect the physiological biomarkers of motor symptoms in PD patients and adaptively trigger the DBS with ...
The authors present the case of patient with subcutaneous fibrosis, which developed around extension cables in patient treated with DBS for essential tremor, 5 months after surgery. ...
doi:10.1159/000448961
pmid:27676399
fatcat:wlmdt6rwsbdmha6vtkin6ywbou
Autoregressive process parameters estimation from Compressed Sensing measurements and Bayesian dictionary learning
2016
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Along with deterministic methods to solve the dictionary learning problem, other authors proposed a probabilistic approach. ...
Along with a low estimation error, we also show that the local estimates at each node reach consensus. ...
With the fast updates for {x q } in hand we can formally state the full Algorithm 4 which we call the Bayesian K-SVD (BKSVD) method. ...
doi:10.6092/polito/porto/2642292
fatcat:4tz7chgiubdlpeaxlcfxfqgdsy
Hop Hill: Culture and Climactic Change in Central Texas
1977
Index of Texas Archaeology Open Access Grey Literature from the Lone Star State
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However, no single indicator correlated exactly with statistical data, leading to the consensus that an interaction was indicated. ...
The first and perhaps easiest problem to deal with is a deterministic mapping of climatic conditions in Texas at different global temperature levels. ...
doi:10.21112/ita.1977.1.20
fatcat:5xkoimqhz5agxgugcp4kx7fbsm
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