Greedy algorithms exploit problem structure and constraints to achieve linear-time performance. Yet there is still no completely satisfactory way of constructing greedy algorithms. For example, the Greedy Algorithm of Edmonds depends upon translating a problem into an algebraic structure called a matroid, but the existence of such a translation can be as hard to determine as the existence of a ...

A coin set is a strictly increasing list of positive integers that always begins with 1. A coin set is called greedy when the simple greedy change-making algorithm always produces the fewest number of coins in change. Here, the greedy changemaking algorithm repeatedly selects the largest denomination coin less than the remaining amount until it has assembled the correct change. Pearson has prov...

We model robust procurement as an optimization problem. We show that its decision version is NP-complete, and propose a backtracking algorithm with cuts that reduce the search-space to find the optimal solution. We then develop a mechanism that motivates agents to truthfully report their private information (i.e., truthful in dominant strategies), maximizes the social welfare (efficient), and e...

Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute a posterior distribution over languages by combi...

The general theory of greedy approximation is well developed. Much less is known about how specific features of a dictionary can be used for our advantage. In this paper we discuss incoherent dictionaries. We build a new greedy algorithm which is called the Orthogonal Super Greedy Algorithm (OSGA). OSGA is more efficient than a standard Orthogonal Greedy Algorithm (OGA). We show that the rates ...

We give a unified view to greedy geometric routing algorithms in ad hoc networks. For this, we first present a general form of greedy routing algorithm using a class of objective functions which are invariant under congruent transformations of a point set. We show that several known greedy routing algorithms such as Greedy Routing, Compass Routing, and Midpoint Routing can be regarded as specia...

Ant Colony Optimization (ACO) algorithms construct solutions each time starting from scratch, that is, from an empty solution. Similar to ACO, Iterated Greedy is a constructive stochastic local search (SLS) method. However, differently from ACO, Iterated Greedy starts the solution construction from partial solutions. In this paper we examine the performance of a variation of MAX–MIN Ant System,...

We introduce a new geometric spanner, δ-Greedy, whose construction is based on a generalization of the known Path-Greedy and Gap-Greedy spanners. The δ-Greedy spanner combines the most desirable properties of geometric spanners both in theory and in practice. More specifically, it has the same theoretical and practical properties as the Path-Greedy spanner: a natural definition, small degree, l...

The contextual bandit literature has traditionally focused on algorithms that address the explorationexploitation tradeoff. In particular, greedy algorithms that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy algorithms are desirable in many practical settings where exploration may be prohibitively costly or unethical (e.g., cli...