Identifiable surfaces in constrained optimization
SIAM Journal on Control and Optimization
Active Sets, Nonsmoothness, and Sensitivity
SIAM Journal on Optimization
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression
The Journal of Machine Learning Research
Efficient projections onto the l1-ball for learning in high dimensions
Proceedings of the 25th international conference on Machine learning
A coordinate gradient descent method for nonsmooth separable minimization
Mathematical Programming: Series A and B
A proximal method for identifying active manifolds
Computational Optimization and Applications
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
SIAM Journal on Optimization
Further results on stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
A Fast Hybrid Algorithm for Large-Scale l1-Regularized Logistic Regression
The Journal of Machine Learning Research
Computational Optimization and Applications
Approximation accuracy, gradient methods, and error bound for structured convex optimization
Mathematical Programming: Series A and B - 20th International Symposium on Mathematical Programming – ISMP 2009
Maximum Block Improvement and Polynomial Optimization
SIAM Journal on Optimization
Manifold identification in dual averaging for regularized stochastic online learning
The Journal of Machine Learning Research
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We discuss minimization of a smooth function to which is added a separable regularization function that induces structure in the solution. A block-coordinate relaxation approach with proximal linearized subproblems yields convergence to critical points, while identification of the optimal manifold (under a nondegeneracy condition) allows acceleration techniques to be applied on a reduced space. The work is motivated by experience with an algorithm for regularized logistic regression, and computational results for the algorithm on problems of this type are presented.