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Found 6 papers, 0 papers with code
Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization
We prove that our algorithm finds an $\epsilon$-stationary point with a computational complexity of $\mathcal O(\epsilon^{-3k_*-5})$, where $k_*$ is the parameter of the Cressie-Read divergence.
Hyperparameter Estimation for Sparse Bayesian Learning Models
Sparse Bayesian Learning (SBL) models are extensively used in signal processing and machine learning for promoting sparsity through hierarchical priors.
Numerical Solution of Fredholm Integral Equations of the Second Kind using Neural Network Models
The coefficients of this linear combination are served as the weights between the hidden layer and the output layer of the neural network while the mean square error between the exact solution and the approximation solution at the training set as the cost function.
The Proximity Operator of the Log-Sum Penalty
The log-sum penalty is often adopted as a replacement for the $\ell_0$ pseudo-norm in compressive sensing and low-rank optimization.
Compressive Sensing
Optimization and Control
49J53, 49J52, 90C26
Finding Dantzig selectors with a proximity operator based fixed-point algorithm
In this paper, we study a simple iterative method for finding the Dantzig selector, which was designed for linear regression problems.
Separation of undersampled composite signals using the Dantzig selector with overcomplete dictionaries
In this paper, we propose using the Dantzig selector model incorporating an overcomplete dictionary to separate a noisy undersampled collection of composite signals, and present an algorithm to efficiently solve the model.
