text: We study the envy free pricing problem faced by a seller who wishes to maximize revenue by setting prices for bundles of items. If there is an unlimited supply of items and agents are single minded then we show that finding the revenue maximizing envy free allocation pricing can be solved in polynomial time by reducing it to an instance of weighted independent set on a perfect graph. We define an allocation pricing as if no agent wishes to replace her allocation with the union of the allocations of some set of other agents and her price with the sum of their prices. We show that it is -hard to decide if a given allocation pricing is multi envy free. We also show that revenue maximization multi envy free allocation pricing is hard. Furthermore, we give efficient algorithms and hardness results for various variants of the highway problem.
  @cite_13: 
  @cite_3: We present approximation and online algorithms for a number of problems of pricing items for sale so as to maximize seller's revenue in an unlimited supply setting. Our first result is an O(k)-approximation algorithm for pricing items to single-minded bidders who each want at most k items. This improves over recent independent work of Briest and Krysta [5] who achieve an O(k2) bound. For the case k = 2, where we obtain a 4-approximation, this can be viewed as the following graph vertex pricing problem: given a (multi) graph G with valuations we on the edges, find prices pi ≥ 0 for the vertices to maximize Σ (pi + pj). e=(i,j):we ≥ pi + pj.We also improve the approximation of [11] from O(log m + log n) to O(log n), where m is the number of bidders and n is the number of items, for the "highway problem" in which all desired subsets are intervals on a line.Our approximation algorithms can be fed into the generic reduction of [2] to yield an incentive-compatible auction with nearly the same performance guarantees so long as the number of bidders is sufficiently large. In addition, we show how our algorithms can be combined with results of Blum and Hartline [3], [4], and Kalai and Vempala [13] to achieve good performance in the online setting, where customers arrive one at a time and each must be presented a set of item prices based only on knowledge of the customers seen so far.
  @cite_11: We deal with the problem of finding profit-maximizing prices for a finite number of distinct goods, assuming that of each good an unlimited number of copies is available, or that goods can be reproduced at no cost (e.g., digital goods). Consumers specify subsets of the goods and the maximum prices they are willing to pay. In the considered single-minded case every consumer is interested in precisely one such subset. If the goods are the edges of a graph and consumers are requesting to purchase paths in this graph, then we can think of the problem as pricing computer network connections or transportation links.We start by showing weak NP-hardness of the very restricted case in which the requested subsets are nested, i.e., contained inside each other or non-intersecting, thereby resolving the previously open question whether the problem remains NP-hard when the underlying graph is simply a line. Using a reduction inspired by this result we present an approximation preserving reduction that proves APX-hardness even for very sparse instances defined on general graphs, where the number of requests per edge is bounded by a constant B and no path is longer than some constant l. On the algorithmic side we first present an O(log l + log B)-approximation algorithm that (almost) matches the previously best known approximation guarantee in the general case, but is especially well suited for sparse problem instances. Using a new upper bounding technique we then give an O(l2)-approximation, which is the first algorithm for the general problem with an approximation ratio that does not depend on B.
summary: For the unlimited supply the problem is NP-hard (Briest and Kriesta @cite_11 ). Also the problem is given @math -approximation by @cite_3 . When the length of each interval is bounded by a constant or the valuation of each agent is bounded by a constant, @cite_13 give a fully polynomial time approximation scheme (FPTAS). If the intervals requested by different agents have a nested structure then an FPTAS is possible @cite_3 @cite_11 .