The generalized minimum spanning tree problem

We consider the Generalized Minimum Spanning Tree Problem denoted by GMSTP. It is known that GMSTP is NP-hard and even finding a near optimal solution is NP-hard. We introduce a new mixed integer programming formulation of the problem which contains a polynomial number of constraints and a polynomial number of variables. Based on this formulation we give an heuristic solution, a lower bound procedure and an upper bound procedure and present the advantages of our approach in comparison with an earlier method. We present a solution procedure for solving GMST problem using cutting planes

An approximation algorithm for the generalized minimum spanning tree problem with bounded cluster size

Given a complete undirected graph with the nodes partitioned into m node sets called clusters, the Generalized Minimum Spanning Tree problem denoted by GMST is to find a minimum-cost tree which includes exactly one node from each cluster. It is known that the GMST problem is NP-hard and even finding a near optimal solution is NP-hard. We give an approximation algorithm for the Generalized Minimum Spanning Tree problem in the case when the cluster size is bounded by $\rho$. In this case, the GMST problem can be approximated to within 2$\rho$

Cadmium sulfide in a Mesoproterozoic terrestrial environment

Peer reviewedPostprin

Alignment and Aperture Scan at the Fermilab Booster

The Fermilab booster has an intensity upgrade plan called the Proton Improvement plan (PIP). The flux throughput goal is 2E17 protons/hour, which is almost double the current operation at 1.1E17 protons/hour. The beam loss in the machine is going to be the source of issues. The booster accelerates beam from 400 MeV to 8 GeV and extracts to the Main Injector. Several percent of the beam is lost within 3 msec after the injection. The aperture at injection energy was measured and compared with the survey data. The magnets are going to be realigned in March 2012 in order to increase the aperture. The beam studies, analysis of the scan and alignment data, and the result of the magnet moves will be discussed in this paper.Comment: 3 pp. 3rd International Particle Accelerator Conference (IPAC 2012) 20-25 May 2012, New Orleans, Louisian

Information theoretic approach to interactive learning

The principles of statistical mechanics and information theory play an important role in learning and have inspired both theory and the design of numerous machine learning algorithms. The new aspect in this paper is a focus on integrating feedback from the learner. A quantitative approach to interactive learning and adaptive behavior is proposed, integrating model- and decision-making into one theoretical framework. This paper follows simple principles by requiring that the observer's world model and action policy should result in maximal predictive power at minimal complexity. Classes of optimal action policies and of optimal models are derived from an objective function that reflects this trade-off between prediction and complexity. The resulting optimal models then summarize, at different levels of abstraction, the process's causal organization in the presence of the learner's actions. A fundamental consequence of the proposed principle is that the learner's optimal action policies balance exploration and control as an emerging property. Interestingly, the explorative component is present in the absence of policy randomness, i.e. in the optimal deterministic behavior. This is a direct result of requiring maximal predictive power in the presence of feedback.Comment: 6 page