## 2 Citations

Nonnegative matrix factorization : complexity, algorithms and applications

- Computer Science
- 2011

This thesis explores a closely related problem, namely nonnegative matrix factorization (NMF), a low-rank matrix approximation problem with nonnegativity constraints, and makes connections with well-known problems in graph theory, combinatorial optimization and computational geometry.

Wideband waveform optimization for energy detector receiver with practical considerations

- Computer Science2009 IEEE International Conference on Ultra-Wideband
- 2009

A number of solutions are provided in this paper in conjunction with numerical verification using measured data on waveform optimization problems raised from advanced radio system prototyping conducted recently.

## 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∗

- Mathematics, Computer Science
- 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

- Computer Science, Mathematics
- 2009

This paper describes algorithms for maximum likelihood estimation of Gaussian graphical models with conditional independence constraints, and presents a dual method suited for graphs that are nearly chordal, and makes several connections between sparse matrix algorithms and the theory of normal graphical model with chordal graphs.

Sparse Principal Component Analysis

- Computer Science
- 2006

This work introduces a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified principal components with sparse loadings and shows that PCA can be formulated as a regression-type optimization problem.

Decoding by linear programming

- Computer ScienceIEEE 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

- 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.

- MathematicsProceedings 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 ∗

- Computer Science
- 2004

A novel structural learning method called HdBCS that performs covariance selection in a Bayesian framework for datasets with tens of thousands of variables and illustrates the use with an example from a large-scale gene expression study of breast cancer.