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Found 10 papers, 4 papers with code
Reconstruction of Functions from Prescribed Proximal Points
Under investigation is the problem of finding the best approximation of a function in a Hilbert space subject to convex constraints and prescribed nonlinear transformations.
Functional Analysis Optimization and Control
c-lasso -- a Python package for constrained sparse and robust regression and classification
The underlying statistical forward model is assumed to be of the following form: \[ y = X \beta + \sigma \epsilon \qquad \textrm{subject to} \qquad C\beta=0 \] Here, $X \in \mathbb{R}^{n\times d}$is a given design matrix and the vector $y \in \mathbb{R}^{n}$ is a continuous or binary response vector.
Fixed Point Strategies in Data Science
The goal of this paper is to promote the use of fixed point strategies in data science by showing that they provide a simplifying and unifying framework to model, analyze, and solve a great variety of problems.
Optimization and Control
Regression models for compositional data: General log-contrast formulations, proximal optimization, and microbiome data applications
This model describes the response as a linear combination of log-ratios of the original compositions and has been extended to the high-dimensional setting via regularization.
Statistics Theory Statistics Theory
Lipschitz Certificates for Neural Network Structures Driven by Averaged Activation Operators
Deriving sharp Lipschitz constants for feed-forward neural networks is essential to assess their robustness in the face of adversarial inputs.
Optimization and Control
Deep Neural Network Structures Solving Variational Inequalities
Motivated by structures that appear in deep neural networks, we investigate nonlinear composite models alternating proximity and affine operators defined on different spaces.
Optimization and Control
Perspective Maximum Likelihood-Type Estimation via Proximal Decomposition
We introduce an optimization model for maximum likelihood-type estimation (M-estimation) that generalizes a large class of existing statistical models, including Huber's concomitant M-estimator, Owen's Huber/Berhu concomitant estimator, the scaled lasso, support vector machine regression, and penalized estimation with structured sparsity.
Statistics Theory Statistics Theory
Classification and regression using an outer approximation projection-gradient method
This paper deals with sparse feature selection and grouping for classification and regression.
Primal-dual splitting algorithm for solving inclusions with mixtures of composite, Lipschitzian, and parallel-sum monotone operators
We propose a primal-dual splitting algorithm for solving monotone inclusions involving a mixture of sums, linear compositions, and parallel sums of set-valued and Lipschitzian operators.
Optimization and Control 47H05, 90C25
Proximal Splitting Methods in Signal Processing
The proximity operator of a convex function is a natural extension of the notion of a projection operator onto a convex set.
Optimization and Control Numerical Analysis 90C25, 65K05, 90C90, 94A08
