The Complexity of Selecting Maximal Solutions

AbstractMany important computational problems involve finding a maximal (with respect to set inclusion) solution in some combinatorial context. We study such maximality problems from the complexity point of view, and categorize their complexity precisely in terms of tight upper and lower bounds. Our results give characterizations of coNP, DP, ΠP2, FPNP||, FNP//OptP [log n] and FPΣP||2 in terms of subclasses of maximality problems. An important consequence of our results is that finding an X-minimal satisfying truth assignment for a given CNF boolean formula is complete for FNP//OptP[log n], solving an open question by Papadimitriou [Proceedings of the 32nd IEEE Symposium on the Foundations of Computer Science, 1991, pp. 163-169]

Duality between Feature Selection and Data Clustering

The feature-selection problem is formulated from an information-theoretic perspective. We show that the problem can be efficiently solved by an extension of the recently proposed info-clustering paradigm. This reveals the fundamental duality between feature selection and data clustering,which is a consequence of the more general duality between the principal partition and the principal lattice of partitions in combinatorial optimization

Making Robust Decisions in Discrete Optimization Problems as a Game against Nature

In this paper a discrete optimization problem under uncertainty is discussed. Solving such a problem can be seen as a game against nature. In order to choose a solution, the minmax and minmax regret criteria can be applied. In this paper an extension of the known minmax (regret) approach is proposed. It is shown how different types of uncertainty can be simultaneously taken into account. Some exact and approximation algorithms for choosing a best solution are constructed.Discrete optimization, minmax, minmax regret, game against nature

Scalable Exact Parent Sets Identification in Bayesian Networks Learning with Apache Spark

In Machine Learning, the parent set identification problem is to find a set of random variables that best explain selected variable given the data and some predefined scoring function. This problem is a critical component to structure learning of Bayesian networks and Markov blankets discovery, and thus has many practical applications, ranging from fraud detection to clinical decision support. In this paper, we introduce a new distributed memory approach to the exact parent sets assignment problem. To achieve scalability, we derive theoretical bounds to constraint the search space when MDL scoring function is used, and we reorganize the underlying dynamic programming such that the computational density is increased and fine-grain synchronization is eliminated. We then design efficient realization of our approach in the Apache Spark platform. Through experimental results, we demonstrate that the method maintains strong scalability on a 500-core standalone Spark cluster, and it can be used to efficiently process data sets with 70 variables, far beyond the reach of the currently available solutions