Abstract
We consider profit maximization pricing problems, where we are given a set of m customers and a set of n items. Each customer c is associated with a subset S c ⊆ [n] of items of interest, together with a budget B c , and we assume that there is an unlimited supply of each item. Once the prices are fixed for all items, each customer c buys a subset of items in S c , according to its buying rule. The goal is to set the item prices so as to maximize the total profit.
We study the unit-demand min-buying pricing (UDP MIN ) and the single-minded pricing (SMP) problems. In the former problem, each customer c buys the cheapest item i ∈ S c , if its price is no higher than the budget B c , and buys nothing otherwise. In the latter problem, each customer c buys the whole set S c if its total price is at most B c , and buys nothing otherwise. Both problems are known to admit \(O(\min \left\{ \log (m+n), n \right\})\)-approximation algorithms. We prove that they are log1 − ε (m + n) hard to approximate for any constant ε, unless \(\mbox{\sf NP} \subseteq{\sf DTIME}(n^{\log^{\delta} n})\), where δ is a constant depending on ε. Restricting our attention to approximation factors depending only on n, we show that these problems are \(2^{\log^{1-\delta} n}\)-hard to approximate for any δ > 0 unless \(\mbox{\sf NP} \subseteq{\sf ZPTIME}(n^{\log^{\delta'} n})\), where δ′ is some constant depending on δ. We also prove that restricted versions of UDP MIN and SMP, where the sizes of the sets S c are bounded by k, are k 1/2 − ε-hard to approximate for any constant ε.
We then turn to the Tollbooth Pricing problem, a special case of SMP, where each item corresponds to an edge in the input graph, and each set S c is a simple path in the graph. We show that Tollbooth Pricing is at least as hard to approximate as the Unique Coverage problem, thus obtaining an Ω(logε n)-hardness of approximation, assuming \(\mbox{\sf NP}\not\subseteq \mbox{\sf BPTIME}(2^{n^{\delta}})\), for any constant δ, and some constant ε depending on δ.
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Chalermsook, P., Chuzhoy, J., Kannan, S., Khanna, S. (2012). Improved Hardness Results for Profit Maximization Pricing Problems with Unlimited Supply. In: Gupta, A., Jansen, K., Rolim, J., Servedio, R. (eds) Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. APPROX RANDOM 2012 2012. Lecture Notes in Computer Science, vol 7408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32512-0_7
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DOI: https://doi.org/10.1007/978-3-642-32512-0_7
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