Greedy and $K$-Greedy Algorithms for Multidimensional Data Association

Simple Greedy Algorithms for Fundamental Multidimensional Graph Problems

We revisit fundamental problems in undirected and directed graphs, such as the problems of computing spanning trees, shortest paths, steiner trees, and spanning arborescences of minimum cost. We assume that there are d different cost functions associated with the edges of the input graph and seek for solutions to the resulting multidimensional graph problems so that the pnorm of the different c...

Greedy algorithms for prediction

In many prediction problems, it is not uncommon that the number of variables used to construct a forecast is of the same order of magnitude as the sample size, if not larger. We then face the problem of constructing a prediction in the presence of potentially large estimation error. Control of the estimation error is either achieved by selecting variables or combining all the variables in some ...

Greedy Algorithms

Achieving Multidimensional K-Anonymity by a Greedy Approach

Protecting privacy in microdata publishing is K-Anonymity, Here recoding “models” have been considered for achieving k anonymity[1,2]. We proposes a new multidimensional model, which gives high flexibility. Often this flexibility leads to higher-quality anonymizations, as measured both by general-purpose metrics and more specific notions of query answerability. Like previous multidimensional mo...

Parallelism and Greedy Algorithms

A number of greedy algorithms are examined <and are shown to be probably inherently sequential. Greedy algorithms are presented for finding a maximal path, for finding a . maximal set of disjoint paths in a layered dag, and for finding the largest induced subgraph of a graph that has all vertices of degree at least Ic. It is shown that for all of these algorithms, the problem of determining if ...