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Primal Subgradient Methods with Predefined Step Sizes. [PDF]
Nesterov Y.
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Minimum Energy Conical Intersection Optimization Using DFT/MRCI(2). [PDF]
Wang TY, Neville SP, Schuurman MS.
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IEEE Transactions on Neural Networks and Learning Systems, 2022
Decomposing data matrix into low-rank plus additive matrices is a commonly used strategy in pattern recognition and machine learning. This article mainly studies the alternating direction method of multiplier (ADMM) with two dual variables, which is used
Hengmin Zhang+7 more
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Decomposing data matrix into low-rank plus additive matrices is a commonly used strategy in pattern recognition and machine learning. This article mainly studies the alternating direction method of multiplier (ADMM) with two dual variables, which is used
Hengmin Zhang+7 more
semanticscholar +1 more source
Nonsmooth analysis of eigenvalues [PDF]
Subdifferentials (limiting Fréchet, Clarke) of the composition \(f\circ \lambda \) of extended real valued permutation invariant functions \(f\) and the eigenvalue vector function \(\lambda \) of a symmetric matrix \(X\) are calculated using the transformation to principles axes.
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Analysis and Optimization of Nonsmooth Arches
SIAM Journal on Control and Optimization, 2002The Kirchoff-Love model for a smooth clamped arch is considered first. A new treatment for that classical model is developed. A variational formulation, based on optimal control theory, is introduced and, using duality-type arguments, the deformation of the arches are explicitly expressed by integral formulas.
Sprekels, Jürgen+2 more
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Nonsmooth analysis of eigenvalues: A summary [PDF]
Subdifferentials (limiting Fréchet, Clarke) of the composition \(f\circ \lambda \) of extended real valued permutation invariant functions \(f\) and the eigenvalue vector function \(\lambda \) of a symmetric matrix \(X\) are calculated using the transformation to principles axes. If \(U\) is an orthogonal matrix, such that \(\text{Diag} ( \lambda ( X))
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Studies in applied mathematics (Cambridge)
This work explores the theoretical and computational impacts of mixed‐type flux conditions and nonsmooth data on boundary/interior layer‐originated singularly perturbed semilinear reaction–diffusion problems.
Shridhar Kumar, Ishwariya R, P. Das
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This work explores the theoretical and computational impacts of mixed‐type flux conditions and nonsmooth data on boundary/interior layer‐originated singularly perturbed semilinear reaction–diffusion problems.
Shridhar Kumar, Ishwariya R, P. Das
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Nonsmooth Nonconvex Stochastic Heavy Ball
Journal of Optimization Theory and Applications, 2023Motivated by the conspicuous use of momentum-based algorithms in deep learning, we study a nonsmooth nonconvex stochastic heavy ball method and show its convergence. Our approach builds upon semialgebraic (definable) assumptions commonly met in practical
Tam Le
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