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...

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...

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...

We consider interactive learning and covering problems, in a setting where actions may incur different costs, depending on the response to the action. We propose a natural greedy algorithm for responsedependent costs. We bound the approximation factor of this greedy algorithm in active learning settings as well as in the general setting. We show that a different property of the cost function co...

Recommendation system is able to shape user demands, which can be used for boosting caching gain. In this paper, we jointly optimize content caching and recommendation at base stations. We first propose a model to capture the impact of recommendation on user demands, which is controlled by a user-specific threshold. We then formulate a joint caching and recommendation problem maximizing the cac...