Ordered Greed

Ordered Greed for N SuperQueens

This is an extension of the Ordered Greed technique introduced by Anderson and Gustafson in [1]. The Ordered Greed algorithm (OG) is a hybrid greedy algorithm using permutations. OG is applicable to many classical optimization problems, including job assignment, graph coloring, bin packing, and satisfiability. Here, OG is applied to a variant of the N Queens problem, the N SuperQueens problem. ...

Allowing Privacy Protection Algorithms to Jump Out of Local Optimums: An Ordered Greed Framework

As more and more person-specific data like health information becomes available, increasing attention is paid to confidentiality and privacy protection. One proposed model of privacy protection is kAnonymity, where a dataset is k-anonymous if each record is identical to at least (k-1) others in the dataset. Our goal is to minimize information loss while transforming a collection of records to s...

The Multi-Commodity Source Location Problems and the Price of Greed

Given a graph G = (V, E), we say that a vertex subset S ⊆ V covers a vertex v ∈ V if the edge-connectivity between S and v is at least a given integer k, and also say that S covers an edge vw ∈ E if v and w are both covered. We propose the multi-commodity source location problem, which is such that given a vertexand edge-weighted graph G, p players each select q vertices, and obtain a profit th...

Hooked!: Buddhist Writings on Greed, Desire, and the Urge to Consume, and: Subverting Greed: Religious Perspectives on the Global Economy (review)

Greed and Fear in Network Reciprocity: Implications for Cooperation among Organizations

Extensive interdisciplinary literatures have built on the seminal spatial dilemmas model, which depicts the evolution of cooperation on regular lattices, with strategies propagating locally by relative fitness. In this model agents may cooperate with neighbors, paying an individual cost to enhance their collective welfare, or they may exploit cooperative neighbors and diminish collective welfar...