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    MP
    2016
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    A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions
    8 years 12 days ago
    A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions
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    Xudong Li, Defeng Sun, Kim-Chuan Toh
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    MP
    2016
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    The continuous knapsack set
    8 years 12 days ago
    The continuous knapsack set
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    We study the convex hull of the continuous knapsack set which consists of a single inequality constraint with n non-negative integer and m non-negative bounded continuous variable...
    Sanjeeb Dash, Oktay Günlük, Laurence A. ...
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    MP
    2016
    51views Information Technology» more  MP 2016»
    Parallel coordinate descent methods for big data optimization
    8 years 12 days ago
    Parallel coordinate descent methods for big data optimization
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    In this work we show that randomized (block) coordinate descent methods can be accelerated by parallelization when applied to the problem of minimizing the sum of a partially sepa...
    Peter Richtárik, Martin Takác
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    MP
    2016
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    A semismooth Newton-CG based dual PPA for matrix spectral norm approximation problems
    8 years 12 days ago
    A semismooth Newton-CG based dual PPA for matrix spectral norm approximation problems
    Download  www.math.nus.edu.sg
    We consider a class of matrix spectral norm approximation problems for finding an affine combination of given matrices having the minimal spectral norm subject to some prescribed ...
    Caihua Chen, Yong-Jin Liu, Defeng Sun, Kim-Chuan T...
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    MP
    2016
    53views Information Technology» more  MP 2016»
    The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent
    8 years 12 days ago
    The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent
    Download  web.stanford.edu
    The alternating direction method of multipliers (ADMM) is now widely used in many fields, and its convergence was proved when two blocks of variables are alternatively updated. It...
    Caihua Chen, Bingsheng He, Yinyu Ye, Xiaoming Yuan
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    MP
    2016
    65views Information Technology» more  MP 2016»
    Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization
    8 years 12 days ago
    Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization
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    Saeed Ghadimi, Guanghui Lan, Hongchao Zhang
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    MP
    2016
    47views Information Technology» more  MP 2016»
    Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm
    8 years 12 days ago
    Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm
    Download  machinelearning.wustl.edu
    We improve a recent guarantee of Bach and Moulines on the linear convergence of SGD for smooth and strongly convex objectives, reducing a quadratic dependence on the strong convex...
    Deanna Needell, Nathan Srebro, Rachel Ward
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    MP
    2016
    59views Information Technology» more  MP 2016»
    Error bounds for mixed integer linear optimization problems
    8 years 12 days ago
    Error bounds for mixed integer linear optimization problems
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    We introduce computable a-priori and a-posteriori error bounds for optimality and feasibility of a point generated as the rounding of an optimal point of the LP relaxation of a mi...
    Oliver Stein
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    MP
    2016
    53views Information Technology» more  MP 2016»
    Solving variational inequalities with monotone operators on domains given by Linear Minimization Oracles
    8 years 12 days ago
    Solving variational inequalities with monotone operators on domains given by Linear Minimization Oracles
    Download  www2.isye.gatech.edu
    The standard algorithms for solving large-scale convex-concave saddle point problems, or, more generally, variational inequalities with monotone operators, are proximal type algor...
    Anatoli Juditsky, Arkadi Nemirovski
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    MP
    2016
    58views Information Technology» more  MP 2016»
    Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
    8 years 12 days ago
    Accelerated proximal stochastic dual coordinate ascent for regularized loss minimization
    Download  jmlr.org
    We introduce a proximal version of the stochastic dual coordinate ascent method and show how to accelerate the method using an inner-outer iteration procedure. We analyze the runt...
    Shai Shalev-Shwartz, Tong Zhang 0001
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