Statistical Physics of Sparse and Dense Models in Optimization and Inference

Book excerpt: Datasets come in a variety of forms and from a broad range of different applications. Typically, the observed data is noisy or in some other way subject to randomness. The recent developments in machine learning have revived the need for exact theoretical limits of probabilistic methods that recover information from noisy data. In this thesis we are concerned with the following two questions: what is the asymptotically best achievable performance? And how can this performance be achieved, i.e., what is the optimal algorithmic strategy? The answer depends on the properties of the data. The problems in this thesis can all be represented as probabilistic graphical models. The generative process of the data determines the structure of the underlying graphical model. The structures considered here are either sparse random graphs or dense (fully connected) models. The above questions can be studied in a probabilistic framework, which leads to an average (or typical) case answer. Such a probabilistic formulation is natural to statistical physics and leads to a formal analogy with problems in disordered systems. In turn, this permits to harvest the methods developed in the study of disordered systems, to attack constraint satisfaction and statistical inference problems. The formal analogy can be exploited as follows. The optimal performance analysis is directly related to the structure of the extrema of the macroscopic free energy. The algorithmic aspects follow from the minimization of the microscopic free energy (that is, the Bethe free energy in this work) which is closely related to message passing algorithms. This thesis is divided into four contributions. First, a statistical physics investigation of the circular coloring problem is carried out that reveals several distinct features. Second, new rigorous upper bounds on the size of minimal contagious sets in random graphs, with bounded maximum degree, are obtained. Third, the phase diagram of the dense Dawid-Skene model is derived by mapping the problem onto low-rank matrix factorization. The associated approximate message passing algorithm is evaluated on real-world data. Finally, the Bayes optimal denoising mean square error is derived for a restricted class of extensive rank matrix estimation problems.