Results 41 to 50 of about 1,319 (105)

The robust isolated calmness of spectral norm regularized convex matrix optimization problems

Open Mathematics
This article aims to provide a series of characterizations of the robust isolated calmness of the Karush-Kuhn-Tucker (KKT) mapping for spectral norm regularized convex optimization problems. By establishing the variational properties of the spectral norm
Yin Ziran, Chen Xiaoyu, Zhang Jihong
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A variational method for quantitative photoacoustic tomography with piecewise constant coefficients

, 2015
In this article, we consider the inverse problem of determining spatially heterogeneous absorption and diffusion coefficients from a single measurement of the absorbed energy (in the steady-state diffusion approximation of light transfer).
Beretta, Elena, Muszkieta, Monika, Naetar, Wolf, Scherzer, Otmar   +3 more
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Geometric approaches to matrix normalization and graph balancing

Forum of Mathematics, Sigma
Normal matrices, or matrices which commute with their adjoints, are of fundamental importance in pure and applied mathematics. In this paper, we study a natural functional on the space of square complex matrices whose global minimizers are normal ...
Tom Needham, Clayton Shonkwiler
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A constrained tropical optimization problem: complete solution and application example

, 2013
The paper focuses on a multidimensional optimization problem, which is formulated in terms of tropical mathematics and consists in minimizing a nonlinear objective function subject to linear inequality constraints.
Krivulin, Nikolai
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Predicting COVID-19 outbreak in India using modified SIRD model

Applied Mathematics in Science and Engineering
In this paper, the existing Susceptible-Infected-Recovered-Deceased (SIRD) compartmental epidemiologic process model is modified for forecasting the coronavirus effect in India.
Sakshi Shringi, Harish Sharma, Pushpa Narayan Rathie, Jagdish Chand Bansal, Atulya Nagar, Daya Lal Suthar   +5 more
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Instability in deep learning – when algorithms cannot compute uncertainty quantifications for neural networks

European Journal of Applied Mathematics
In deep learning, interval neural networks are used to quantify the uncertainty of a pre-trained neural network. Suppose we are given a computational problem $P$ and a pre-trained neural network $\Phi _P$ that aims to solve $P$ .
Luca Eva Gazdag, Vegard Antun, Anders C. Hansen   +2 more
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An MBO method for modularity optimisation based on total variation and signless total variation

European Journal of Applied Mathematics
In network science, one of the significant and challenging subjects is the detection of communities. Modularity [1] is a measure of community structure that compares connectivity in the network with the expected connectivity in a graph sampled from a ...
Zijun Li, Yves van Gennip, Volker John
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A Fixed Point Theorem for Discontinuous Functions [PDF]

AMS classifications: 54H25, 65K10, 49J53, 68W25Fixed point;simplicial subdivision;discontinuity ...
Herings, P.J.J., Laan, G. van der, Talman, A.J.J., Yang, Z.F.   +3 more
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An Optimization Based Empirical Mode Decomposition Scheme for Images [PDF]

, 2012
Bidimensional empirical mode decompositions (BEMD) have been developed to decompose any bivariate function or image additively into multiscale components, so-called intrinsic mode functions (IMFs), which are approximately orthogonal to each other with ...
Huang, Boqiang, Kunoth, Angela
core  

A smoothing-type algorithm for solving inequalities under the order induced by a symmetric cone

Journal of Inequalities and Applications, 2011
In this article, we consider the numerical method for solving the system of inequalities under the order induced by a symmetric cone with the function involved being monotone. Based on a perturbed smoothing function, the underlying system of inequalities
Zhang Ying, Lu Nan
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