Sparse Learning of the Disease Severity Score for High-Dimensional Data
Complexity, 2017 Learning disease severity scores automatically from collected measurements may aid in the quality of both healthcare and scientific understanding.
Ivan Stojkovic, Zoran Obradovic
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, 2018 We consider a class of nonconvex nonsmooth optimization problems whose objective is the sum of a smooth function and a finite number of nonnegative proper closed possibly nonsmooth functions (whose proximal mappings are easy to compute), some of which ...
Liu, Tianxiang +2 more
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Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees
, 2020 Asynchronous distributed algorithms are a popular way to reduce synchronization costs in large-scale optimization, and in particular for neural network training.
Alistarh, Dan +3 more
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Nonconvex Nonsmooth Low-Rank Minimization via Iteratively Reweighted Nuclear Norm
, 2015 The nuclear norm is widely used as a convex surrogate of the rank function in compressive sensing for low rank matrix recovery with its applications in image recovery and signal processing.
Lin, Zhouchen +3 more
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Decomposition by Successive Convex Approximation: A Unifying Approach for Linear Transceiver Design in Heterogeneous Networks [PDF]
, 2015 We study the downlink linear precoder design problem in a multi-cell dense heterogeneous network (HetNet). The problem is formulated as a general sum-utility maximization (SUM) problem, which includes as special cases many practical precoder design ...
Hong, Mingyi +3 more
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Exact l$_1$ penalty function for nonsmooth multiobjective interval-valued problems
KybernetikaSummary: Our objective in this article is to explore the idea of an unconstrained problem using the exact l\(_1\) penalty function for the nonsmooth multiobjective interval-valued problem (MIVP) having inequality and equality constraints. First of all, we figure out the KKT-type optimality conditions for the problem (MIVP).
Khatri, Julie, Prasad, Ashish Kumar
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Journal of MathematicsThis paper defines a strong convertible nonconvex (SCN) function for solving the unconstrained optimization problems with the nonconvex or nonsmooth (nondifferentiable) function.
Min Jiang +3 more
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The Exact l 1 Penalty Function Method for Constrained Nonsmooth Invex Optimization Problems [PDF]
, 2013 The exactness of the penalization for the exact l1 penalty function method used for solving nonsmooth constrained optimization problems with both inequality and equality constraints is considered. Thus, the equivalence between the sets of optimal solutions in the nonsmooth constrained optimization problem and its associated penalized optimization ...
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The exact absolute value penalty function method for identifying strict global minima of order m in nonconvex nonsmooth programming [PDF]
Optimization Letters, 2015 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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An Adaptive Primal-Dual Framework for Nonsmooth Convex Minimization
, 2018 We propose a new self-adaptive, double-loop smoothing algorithm to solve composite, nonsmooth, and constrained convex optimization problems. Our algorithm is based on Nesterov's smoothing technique via general Bregman distance functions.
Alacaoglu, Ahmet +3 more
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