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Found 9 papers, 1 papers with code
Safe screening rules for L0-regression
We give safe screening rules to eliminate variables from regression with L0 regularization or cardinality constraint.
Parabolic Relaxation for Quadratically-constrained Quadratic Programming -- Part II: Theoretical & Computational Results
In the first part of this work [32], we introduce a convex parabolic relaxation for quadratically-constrained quadratic programs, along with a sequential penalized parabolic relaxation algorithm to recover near-optimal feasible solutions.
Parabolic Relaxation for Quadratically-constrained Quadratic Programming -- Part I: Definitions & Basic Properties
For general quadratically-constrained quadratic programming (QCQP), we propose a parabolic relaxation described with convex quadratic constraints.
Safe Screening for Logistic Regression with $\ell_0$-$\ell_2$ Regularization
In logistic regression, it is often desirable to utilize regularization to promote sparse solutions, particularly for problems with a large number of features compared to available labels.
Enhanced Modeling of Contingency Response in Security-constrained Optimal Power Flow
This paper provides an enhanced modeling of the contingency response that collectively reflects high-fidelity physical and operational characteristics of power grids.
Supermodularity and valid inequalities for quadratic optimization with indicators
We show that the convex hull of the epigraph of the quadratic can be obtaining from inequalities for the underlying supermodular set function by lifting them into nonlinear inequalities in the original space of variables.
Submodular Function Minimization and Polarity
Using polarity, we give an outer polyhedral approximation for the epigraph of set functions.
Rank-one Convexification for Sparse Regression
Sparse regression models are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance.
Sparse and Smooth Signal Estimation: Convexification of L0 Formulations
Signal estimation problems with smoothness and sparsity priors can be naturally modeled as quadratic optimization with $\ell_0$-"norm" constraints.
