Simplifying Random Satisfiability Problem by Removing Frustrating Interactions

How can we remove some interactions in a constraint satisfaction problem (CSP) such that it still remains satisfiable? In this paper we study a modified survey propagation algorithm that enables us to address this question for a prototypical CSP, i.e. random K-satisfiability problem. The average number of removed interactions is controlled by a tuning parameter in the algorithm. If the original problem is satisfiable then we are able to construct satisfiable subproblems ranging from the original one to a minimal one with minimum possible number of interactions. The minimal satisfiable subproblems will provide directly the solutions of the original problem.Comment: 21 pages, 16 figure

On Capturing the Spreading Dynamics over Trading Prices in the Market

While market is a social field where information flows over the interacting agents, there have been not so many methods to observe the spreading information in the prices comprising the market. By incorporating the entropy transfer in information theory in its relation to the Granger causality, the paper proposes a tree of weighted directed graph of market to detect the changes of price might affect other price changes. We compare the proposed analysis with the similar tree representation built from the correlation coefficients of stock prices in order to have insight of possibility in seeing the collective behavior of the market in general.Comment: 9 pages, 3 figure

Lipschitz Continuous Algorithms for Graph Problems

It has been widely observed in the machine learning community that a small perturbation to the input can cause a large change in the prediction of a trained model, and such phenomena have been intensively studied in the machine learning community under the name of adversarial attacks. Because graph algorithms also are widely used for decision making and knowledge discovery, it is important to design graph algorithms that are robust against adversarial attacks. In this study, we consider the Lipschitz continuity of algorithms as a robustness measure and initiate a systematic study of the Lipschitz continuity of algorithms for (weighted) graph problems. Depending on how we embed the output solution to a metric space, we can think of several Lipschitzness notions. We mainly consider the one that is invariant under scaling of weights, and we provide Lipschitz continuous algorithms and lower bounds for the minimum spanning tree problem, the shortest path problem, and the maximum weight matching problem. In particular, our shortest path algorithm is obtained by first designing an algorithm for unweighted graphs that are robust against edge contractions and then applying it to the unweighted graph constructed from the original weighted graph. Then, we consider another Lipschitzness notion induced by a natural mapping that maps the output solution to its characteristic vector. It turns out that no Lipschitz continuous algorithm exists for this Lipschitz notion, and we instead design algorithms with bounded pointwise Lipschitz constants for the minimum spanning tree problem and the maximum weight bipartite matching problem. Our algorithm for the latter problem is based on an LP relaxation with entropy regularization