Low-rank matrix recovery (LMR) is a rank minimization problem subject to linear equality constraints, and it arises in many fields such as signal and image processing, statistics, computer vision, system identification and control. This class of optimization problems is generally NP-hard. A popular approach replaces the rank function with the nuclear norm of the matrix variable. In this paper, ...

Map building is a classical problem in mobile and autonomous robotics, and sensor models is a way to interpret raw sensory information, especially for building maps. In this paper we propose a parameterized sensor model, and optimize map goodness with respect to these parameters. A new approach, measuring the goodness of maps without a handcrafted map of the actual environment is introduced and...

In this paper, we develop verifiable and computable performance analysis of sparsity recovery. We define a family of goodness measures for arbitrary sensing matrices as a set of optimization problems, and design algorithms with a theoretical global convergence guarantee to compute these goodness measures. The proposed algorithms solve a series of second-order cone programs, or linear programs. ...

Many works have been carried out to compare the efficiency of several goodness of fit procedures for identifying whether or not a particular distribution could adequately explain a data set. In this paper a study is conducted to investigate the power of several goodness of fit tests such as Kolmogorov Smirnov (KS), Anderson-Darling(AD), CramervonMises (CV) and a proposed modification of Kolmogo...

We examine the power of different exact tests of differentiation for diploid populations. Since there is not necessarily random mating within populations, the appropriate hypothesis to construct exact tests is that of independent sampling of genotypes. There are two categories of tests, FST-estimator tests and goodness of fit tests. In this latter category, we distinguish "allelic statistics", ...