On profit-maximizing envy-free pricing
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Mechanism Design via Machine Learning
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Combination can be hard: approximability of the unique coverage problem
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Approximation algorithms and online mechanisms for item pricing
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Buying cheap is expensive: hardness of non-parametric multi-product pricing
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Designing and learning optimal finite support auctions
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Near-optimal pricing in near-linear time
WADS'05 Proceedings of the 9th international conference on Algorithms and Data Structures
Optimal envy-free pricing with metric substitutability
Proceedings of the 9th ACM conference on Electronic commerce
Simple versus optimal mechanisms
Proceedings of the 10th ACM conference on Electronic commerce
On Stackelberg Pricing with Computationally Bounded Consumers
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Multi-parameter mechanism design and sequential posted pricing
Proceedings of the forty-second ACM symposium on Theory of computing
Budget constrained auctions with heterogeneous items
Proceedings of the forty-second ACM symposium on Theory of computing
Proceedings of the 11th ACM conference on Electronic commerce
The power of randomness in bayesian optimal mechanism design
Proceedings of the 11th ACM conference on Electronic commerce
Pricing randomized allocations
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
The power of uncertainty: bundle-pricing for unit-demand customers
WAOA'10 Proceedings of the 8th international conference on Approximation and online algorithms
On optimal single-item auctions
Proceedings of the forty-third annual ACM symposium on Theory of computing
On optimal multidimensional mechanism design
ACM SIGecom Exchanges
Optimal Envy-Free Pricing with Metric Substitutability
SIAM Journal on Computing
Bayesian optimal auctions via multi- to single-agent reduction
Proceedings of the 13th ACM Conference on Electronic Commerce
Mechanisms and allocations with positive network externalities
Proceedings of the 13th ACM Conference on Electronic Commerce
Symmetries and optimal multi-dimensional mechanism design
Proceedings of the 13th ACM Conference on Electronic Commerce
Buying Cheap Is Expensive: Approximability of Combinatorial Pricing Problems
SIAM Journal on Computing
WINE'12 Proceedings of the 8th international conference on Internet and Network Economics
Pricing public goods for private sale
Proceedings of the fourteenth ACM conference on Electronic commerce
On revenue maximization for agents with costly information acquisition: extended abstract
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II
Online pricing for bundles of multiple items
Journal of Global Optimization
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Algorithmic pricing is the computational problem that sellers (e.g.,in supermarkets) face when trying to set prices for their items to maximize their profit in the presence of a known demand. Guruswami etal. (SODA, 2005) proposed this problem and gave logarithmic approximations (in the number of consumers) for the unit-demand and single-parameter cases where there is a specific set of consumers and their valuations for bundles are known precisely. Subsequently several versions of the problem have been shown to have poly-logarithmic in approximability. This problem has direct ties to the important open question of better understanding the Bayesian optimal mechanism in multi-parameter agent settings; however, for this purpose approximation factors logarithmic in the number of agents are inadequate. It is therefore of vital interest to consider special cases where constant approximations are possible. We consider the unit-demand variant of this pricing problem. Here a consumer has a valuation for each different item and their value for aset of items is simply the maximum value they have for any item in the set. Instead of considering a set of consumers with precisely known preferences, like the prior algorithmic pricing literature, we assume that the preferences of the consumers are drawn from a distribution. This is the standard assumption in economics; furthermore, the setting of a specific set of customers with specific preferences, which is employed in all of the prior work in algorithmic pricing, is a special case of this general Bayesian pricing problem, where there is a discrete Bayesian distribution for preferences specified by picking one consumer uniformly from the given set of consumers. Notice that the distribution over the valuations for the individual items that this generates is obviously correlated. Our work complements these existing works by considering the case where the consumer's valuations for the different items are independent random variables. Our main result is a constant approximation algorithm for this problem that makes use of an interesting connection between this problem and the concept of virtual valuations from the single-parameter Bayesian optimal mechanism design literature.