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Found 2 papers, 1 papers with code
A Unified Framework for Sparse Relaxed Regularized Regression: SR3
We demonstrate the advantages of SR3 (computational efficiency, higher accuracy, faster convergence rates, greater flexibility) across a range of regularized regression problems with synthetic and real data, including applications in compressed sensing, LASSO, matrix completion, TV regularization, and group sparsity.
Greedy Sensor Placement with Cost Constraints
The problem of optimally placing sensors under a cost constraint arises naturally in the design of industrial and commercial products, as well as in scientific experiments.
Optimization and Control
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