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A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
We propose, analyze, and test an alternating minimization algorithm for recovering images from blurry and noisy observations with total variation (TV) regularization. This algorithm arises from a newExpand
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EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization
Recently, there has been growing interest in solving consensus optimization problems in a multiagent network. In this paper, we develop a decentralized algorithm for the consensus optimizationExpand
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A feasible method for optimization with orthogonality constraints
Minimization with orthogonality constraints (e.g., $$X^\top X = I$$) and/or spherical constraints (e.g., $$\Vert x\Vert _2 = 1$$) has wide applications in polynomial optimization, combinatorialExpand
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Iteratively reweighted algorithms for compressive sensing
The theory of compressive sensing has shown that sparse signals can be reconstructed exactly from many fewer measurements than traditionally believed necessary. In [1], it was shown empirically thatExpand
  • 1,058
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Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm
  • Zaiwen Wen, Wotao Yin, Yin Zhang
  • Mathematics, Computer Science
  • Math. Program. Comput.
  • 13 July 2012
The matrix completion problem is to recover a low-rank matrix from a subset of its entries. The main solution strategy for this problem has been based on nuclear-norm minimization which requiresExpand
  • 540
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A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion
This paper considers regularized block multiconvex optimization, where the feasible set and objective function are generally nonconvex but convex in each block of variables. It also accepts nonconvexExpand
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An Iterative Regularization Method for Total Variation-Based Image Restoration
We introduce a new iterative regularization procedure for inverse problems based on the use of Bregman distances, with particular focus on problems arising in image processing. We are motivated byExpand
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Total variation models for variable lighting face recognition
In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength,Expand
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An efficient augmented Lagrangian method with applications to total variation minimization
Based on the classic augmented Lagrangian multiplier method, we propose, analyze and test an algorithm for solving a class of equality-constrained non-smooth optimization problems (chiefly but notExpand
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On the Linear Convergence of the ADMM in Decentralized Consensus Optimization
In decentralized consensus optimization, a connected network of agents collaboratively minimize the sum of their local objective functions over a common decision variable, where their informationExpand
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