## Topics from this paper

## 2 Citations

Nonnegative matrix factorization : complexity, algorithms and applications

- Mathematics
- 2011

Linear dimensionality reduction techniques such as principal component analysis are powerful tools for the analysis of high-dimensional data. In this thesis, we explore a closely related problem,…

Wideband waveform optimization for energy detector receiver with practical considerations

- Engineering2009 IEEE International Conference on Ultra-Wideband
- 2009

This paper deals with waveform optimization problems raised from advanced radio system prototyping conducted recently. Motivated by increasing demands for wireless sensor networks, simple receivers…

## References

SHOWING 1-10 OF 31 REFERENCES

PROX-METHOD WITH RATE OF CONVERGENCE O(1/t) FOR VARIATIONAL INEQUALITIES WITH LIPSCHITZ CONTINUOUS MONOTONE OPERATORS AND SMOOTH CONVEX-CONCAVE SADDLE POINT PROBLEMS∗

- 2004

We propose a prox-type method with efficiency estimate O( −1) for approximating saddle points of convex-concave C1,1 functions and solutions of variational inequalities with monotone Lipschitz…

Covariance selection

- Biometrics
- 1972

Maximum likelihood estimation of Gaussian graphical models : Numerical implementation and topology selection

- 2009

We describe algorithms for maximum likelihood estimation of Gaussian graphical models with conditional independence constraints. It is well-known that this problem can be formulated as an…

Sparse Principal Component Analysis

- Mathematics
- 2006

Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the…

Decoding by linear programming

- Computer Science, MathematicsIEEE Transactions on Information Theory
- 2005

F can be recovered exactly by solving a simple convex optimization problem (which one can recast as a linear program) and numerical experiments suggest that this recovery procedure works unreasonably well; f is recovered exactly even in situations where a significant fraction of the output is corrupted.

Smooth minimization of non-smooth functions

- Mathematics, Computer ScienceMath. Program.
- 2005

A new approach for constructing efficient schemes for non-smooth convex optimization is proposed, based on a special smoothing technique, which can be applied to functions with explicit max-structure, and can be considered as an alternative to black-box minimization.

Smooth minimization of nonsmooth functions

- Mathematical Programming,
- 2005

Sparse nonnegative solution of underdetermined linear equations by linear programming.

- Mathematics, MedicineProceedings of the National Academy of Sciences of the United States of America
- 2005

It is shown that outward k-neighborliness is equivalent to the statement that, whenever y = Ax has a non negative solution with at most k nonzeros, it is the nonnegative solution to y =Ax having minimal sum.

Bayesian Covariance Selection ∗

- 2004

We present a novel structural learning method called HdBCS that performs covariance selection in a Bayesian framework for datasets with tens of thousands of variables. HdBCS is based on the intrinsic…