Low-rank optimization on the cone of positive semidefinite matrices

We propose an algorithm for solving optimization problems defined on a subset of the cone of symmetric positive semidefinite matrices. This algorithm relies on the factorization X = Y Y T , where the number of columns of Y fixes an upper bound on the rank of the positive semidefinite matrix X. It is thus very effective for solving problems that have a low-rank solution. The factorization X = Y Y T leads to a reformulation of the original problem as an optimization on a particular quotient manifold. The present paper discusses the geometry of that manifold and derives a second-order optimization method with guaranteed quadratic convergence. It furthermore provides some conditions on the rank of the factorization to ensure equivalence with the original problem. In contrast to existing methods, the proposed algorithm converges monotonically to the sought solution. Its numerical efficiency is evaluated on two applications: the maximal cut of a graph and the problem of sparse principal component analysis. © 2010 Society for Industrial and Applied Mathematics.

Effect of polymer concentration on stabilized large-tilt-angle flexoelectro-optic switching

In this letter, the uniform lying helix (ULH) liquid crystal texture, required for the flexoelectro-optic effect, is polymer stabilized by the addition of a small percentage of reactive mesogen to a high-tilt-angle (φ>60°) bimesogenic chiral nematic host. The electro-optic response is measured for a range of reactive mesogen concentration mixtures, and compared to the large-tilt-angle switch of the pure chiral nematic mixture. The optimum concentration of reactive mesogen, which is found to provide ample stabilization of the texture with minimal impact on the electro-optic response, is found to be approximately 3%. Our results indicate that polymer stabilization of the ULH texture using a very low concentration of reactive mesogen is a reliable way of ruggedizing flexoelectro-optic devices without interfering significantly with the electro-optics of the effect, negating the need for complicated surface alignment patterns or surface-only polymerization. The polymer stabilization is shown to reduce the temperature dependence of the flexoelectro-optic response due to "pinning" of the chiral nematic helical pitch. This is a restriction of the characteristic thermochromic behavior of the chiral nematic. Furthermore, selection of the temperature at which the sample is ultraviolet cured allows the tilt angle to be optimized for the entire chiral nematic temperature range. The response time, however, remains more sensitive to operating temperature than curing temperature. This allows the sample to be cured at low temperature and operated at high temperature, providing simultaneous optimization of these two previously antagonistic performance aspects. © 2006 American Institute of Physics.

A species conserving genetic algorithm for multimodal function optimization

This paper introduces a new technique called species conservation for evolving parallel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current generation are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.

Optimization of dynamic properties and link performance for directly-modulated gain-coupled DFB lasers

This paper demonstrates the respective roles that combined gain- and index-coupling play in the dynamic properties and overall link performance of DFB lasers. It is shown that for datacommunication applications, modest gain-coupling enables optimum transmission at 10Gbit/s.

Geometric optimization methods for the analysis of gene expression data

DNA microarrays provide such a huge amount of data that unsupervised methods are required to reduce the dimension of the data set and to extract meaningful biological information. This work shows that Independent Component Analysis (ICA) is a promising approach for the analysis of genome-wide transcriptomic data. The paper first presents an overview of the most popular algorithms to perform ICA. These algorithms are then applied on a microarray breast-cancer data set. Some issues about the application of ICA and the evaluation of biological relevance of the results are discussed. This study indicates that ICA significantly outperforms Principal Component Analysis (PCA).