Two methods for incorporating a time-invariant, high-rank covariance estimate in an ensemble-based data assimilation system global weather prediction are compared. The hybrid-covariance approach linearly combines the static and four-dimensional variational solver, whereas hybrid-gain blends analysis increments computed separately using three-dimensional solution ensemble Kalman filter solution....

The matrix rank minimization problem has applications in many fields such as system identification, optimal control, low-dimensional embedding, etc. As this problem is NP-hard in general, its convex relaxation, the nuclear norm minimization problem, is often solved instead. Recently, Ma, Goldfarb and Chen proposed a fixed-point continuation algorithm for solving the nuclear norm minimization pr...

We consider high dimensional dynamic multi-product pricing with an evolving but low-dimensional linear demand model. Assuming the temporal variation in cross-elasticities exhibits low-rank structure based on fixed (latent) features of the products, we show that the revenue maximization problem reduces to an online bandit convex optimization with side information given by the observed demands. W...

Ranking of a company's financial information is one of the most important tools for identifying strengths and weaknesses and identifying opportunities and threats outside the company. In this study, it is attempted to examine the financial statements of companies to rank and explain the transparency of financial information of 198 companies during 2009-2017 using artificial intelligence and neu...

We propose efficient algorithms for simultaneous clustering and completion of incomplete high-dimensional data that lie in a union of low-dimensional subspaces. We cast the problem as finding a completion of the data matrix so that each point can be reconstructed as a linear or affine combination of a few data points. Since the problem is NP-hard, we propose a lifting framework and reformulate ...

Most engineers are (indirectly) trained to be “feedforward thinkers” and they immediately think of “model inversion” when it comes to doing control. Thus, they prefer to rely on models instead of data, although feedback solutions in most cases are much simpler and more robust∗.