Approximating Concavely Parameterized Optimization Problems

By Joachim Giesen, Jens K. Müller, Sören Laue, Sascha Swiercy. 2012

26 citations recorded by the source corpus.

Subfields: Optimization & Training

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Optimization & Training

1. Simon Lacoste-Julien
2. Jean-Baptiste Alayrac
3. Martin Jaggi
4. Anton Osokin
5. Puneet Kumar Dokania
6. Isabella Lukasewitz
7. Peter Richtárik
8. Francis R. Bach

Citations in the corpus

• Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs (2016, methodology): “Another difference to (Giesen et al., 2012) consists in using the λ-formulation of SVM instead of the C-formulation.”
• An Exponential Lower Bound on the Complexity of Regularization Paths (2009, background): “More recently, [18, 27, 19] have proposed to study approximate solution paths (with some continuous guarantee, e.”
• An Equivalence between the Lasso and Support Vector Machines (2013, methodology): “More precisely, for problems of the form minx∈4 f(x, t), general path following methods are available that can maintain an approximation guarantee along the entire path in the parameter t, as shown in [16] and more recently strengthened by [17].”