A note on the solution of bilinear programming problems by reduction to concave minimization
Mathematical Programming: Series A and B - Special Issue: Essays on Nonconvex Optimization
A computational analysis of LCP methods for bilinear and concave quadratic programming
Computers and Operations Research
On affine scaling algorithms for nonconvex quadratic programming
Mathematical Programming: Series A and B
Hidden convexity in some nonconvex quadratically constrained quadratic programming
Mathematical Programming: Series A and B
On the complexity of approximating a KKT point of quadratic programming
Mathematical Programming: Series A and B
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Introduction to Linear Optimization
Introduction to Linear Optimization
A Simplicial Branch-and-Bound Method for Solving Nonconvex All-Quadratic Programs
Journal of Global Optimization
Approximating Global Quadratic Optimization with Convex Quadratic Constraints
Journal of Global Optimization
Dual Bounds and Optimality Cuts for All-Quadratic Programs with Convex Constraints
Journal of Global Optimization
Branch-and-bound approaches to standard quadratic optimization problems
Journal of Global Optimization
Solving Standard Quadratic Optimization Problems via Linear, Semidefinite and Copositive Programming
Journal of Global Optimization
New Results on Quadratic Minimization
SIAM Journal on Optimization
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Convex Optimization
A polyhedral study of nonconvex quadratic programs with box constraints
Mathematical Programming: Series A and B
A branch-and-cut algorithm for nonconvex quadratic programs with box constraints
Mathematical Programming: Series A and B
Decomposition Methods for Solving Nonconvex Quadratic Programs via Branch and Bound*
Journal of Global Optimization
A finite branch-and-bound algorithm for nonconvex quadratic programming via semidefinite relaxations
Mathematical Programming: Series A and B
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
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Sparsity plays an important role in many fields of engineering. The cardinality penalty function, often used as a measure of sparsity, is neither continuous nor differentiable and therefore smooth optimization algorithms cannot be applied directly. In this paper we present a continuous yet non-differentiable sparsity function which constitutes a tight lower bound on the cardinality function. The novelty of this approach is that we cast the problem of minimizing the new sparsity function as a problem with a bilinear objective function. We present a numerical comparison to other sparsity encouraging penalty functions for several applications. Additionally, we apply the techniques developed to minimize an objective function with a truncated hinge loss function. We present highly competitive results for all of the applications.