Multiplicative Updates for Nonnegative Quadratic Programming

DOI10.1162/neco.2007.19.8.2004zbMath1161.90456OpenAlexW2000023271WikidataQ43434410 ScholiaQ43434410MaRDI QIDQ5440961

Fei Sha, Yuanqing Lin, Lawrence K. Saul, Daniel D. Lee

Publication date: 5 February 2008

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco.2007.19.8.2004

zbMATH Keywords

convergenceglobal minimum

Mathematics Subject Classification ID

Convex programming (90C25) Quadratic programming (90C20)

Related Items (20)

Nonnegative-Lasso and application in index tracking ⋮ Nonnegative adaptive Lasso for ultra-high dimensional regression models and a two-stage method applied in financial modeling ⋮ Constrained TV\(_p\)-\(\ell_2\) model for image restoration ⋮ A Proof of Convergence of the Concave-Convex Procedure Using Zangwill's Theory ⋮ Nonnegative elastic net and application in index tracking ⋮ \(l_1\) regularized multiplicative iterative path algorithm for non-negative generalized linear models ⋮ Nonnegative estimation and variable selection via adaptive elastic-net for high-dimensional data ⋮ Multi-class support vector machine classifiers using intrinsic and penalty graphs ⋮ Log-domain interior-point methods for convex quadratic programming ⋮ Variable selection and regularization via arbitrary rectangle-range generalized elastic net ⋮ Multiplicative updates for polynomial root finding ⋮ Exemplar-based low-rank matrix decomposition for data clustering ⋮ Global convergence of modified multiplicative updates for nonnegative matrix factorization ⋮ Adaptive Structure Concept Factorization for Multiview Clustering ⋮ Unnamed Item ⋮ Hyper-graph regularized discriminative concept factorization for data representation ⋮ Path-Based Mathematical Morphology on Tensor Fields ⋮ Nonnegative estimation and variable selection under minimax concave penalty for sparse high-dimensional linear regression models ⋮ Multiplicative update rules for incremental training of multiclass support vector machines ⋮ Projection-free parallel quadratic programming for linear model predictive control

Cites Work

• Exponentiated gradient versus gradient descent for linear predictors
• Gradient projection methods for quadratic programs and applications in training support vector machines
• Learning the parts of objects by non-negative matrix factorization
• Generalized Iterative Scaling for Log-Linear Models

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