Towards a universal test suite for combinatorial auction algorithms

Leyton‐Brown, Kevin; Pearson, Mark; Shoham, Yoav

doi:10.1145/352871.352879

Cited by 252 publications

(183 citation statements)

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“…Matrix bidding can therefore be a quick way to generate examples from a fairly general class of preferences, including preferences (such as the k-of preferences) that would take an exponential amount of bid information in other combinatorial auction data sets, such as those using flat-bids (for example CATS, Leyton-Brown et al, 2000). However, some care must be taken that data is generated in an orderly fashion for the resulting matrix bids to represent economically meaningful behaviors.…”

Section: Discussionmentioning

confidence: 99%

“…While Appendix B describes the exact method for simulating each behavior type, and we do note here that our matrix-bid-based method provides an interesting alternative to the CATS data (see Leyton-Brown et al, 2000). While both methods generate data based on economic models of synergy and predefined preference structures, the compactness of matrix bidding allows for the expression of economically simple preferences that require an exponential number of flat-bids in the XOR language used by CATS.…”

Section: Computational Benefits Of Matrix Biddingmentioning

confidence: 99%

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Matrix Bidding in Combinatorial Auctions

Day

Raghavan

2009

Operations Research

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In a combinational auction in which bidders can bid on any combination of goods, bid data can be of exponential size. We describe an innovative new combinatorial auction format in which bidders submit "matrix bids". The advantage of this approach is that it provides bidders a mechanism to compactly express bids on every possible bundle. We describe many different types of preferences that can be modeled using a matrix bid, which is quite flexible, supporting additive, subadditive, and superadditive preferences simultaneously. To utilize the compactness of the matrix bid format in a more general preference environment, we describe a logical language with matrix bids as "atoms", and show that matrix bids compactly express preferences that require an exponential number of atoms in other bidding languages, and is as expressive as the most sophisticated languages in the literature, (Boutilier, 2002). We model the N P-hard winner-determination problem as a polynomially-sized integer program, specifically an assignment problem with side constraints. We show the strength of this formulation with which we rapidly solve winner determination problems with 72 unique items, indicating that this model may be well suited for practical implementation.

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Section: Discussionmentioning

confidence: 99%

Section: Computational Benefits Of Matrix Biddingmentioning

confidence: 99%

Matrix Bidding in Combinatorial Auctions

Day

Raghavan

2009

Operations Research

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“…The benchmark set is composed of 2 000 MIP-encoded instances (200 goods, 1000 bids) of the NP-hard winner determination problem for combinatorial auctions [45] previously used by Hutter et al [39]. The benchmark set is split in two disjoint sets of 1 000 instances each, one is used for training and the other for testing.…”

Section: Methodsmentioning

confidence: 99%

Automatically improving the anytime behaviour of optimisation algorithms

López-Ibáñez

Stützle

2014

European Journal of Operational Research

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Optimisation algorithms with good anytime behaviour try to return as high-quality solutions as possible independently of the computation time allowed. Designing algorithms with good anytime behaviour is a difficult task, because performance is often evaluated subjectively, by plotting the trade-off curve between computation time and solution quality. Yet, the trade-off curve may be modelled also as a set of mutually nondominated, bi-objective points. Using this model, we propose to combine an automatic configuration tool and the hypervolume measure, which assigns a single quality measure to a nondominated set. This allows us to improve the anytime behaviour of optimisation algorithms by means of automatically finding algorithmic configurations that produce the best nondominated sets. Moreover, the recently proposed weighted hypervolume measure is used here to incorporate the decision-maker's preferences into the automatic tuning procedure. We report on the improvements reached when applying the proposed method to two relevant scenarios: (i ) the design of parameter variation strategies for MAX-MIN Ant System, and (ii ) the tuning of the anytime behaviour of SCIP, an open-source mixed integer programming solver with more than 200 parameters.

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“…We tested CABOB and CPLEX on the common combinatorial auction benchmarks distributions: those presented in Sandholm (2002a), and the Combinatorial Auction Test Suite CATS distributions (Leyton-Brown et al 2000a). In addition, we tested them on new distributions.…”

Section: Methodsmentioning

confidence: 99%

CABOB: A Fast Optimal Algorithm for Winner Determination in Combinatorial Auctions

Sandholm

Suri

Gilpin³

et al. 2005

Management Science

229

180

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C ombinatorial auctions where bidders can bid on bundles of items can lead to more economically efficient allocations, but determining the winners is -complete and inapproximable. We present CABOB, a sophisticated optimal search algorithm for the problem. It uses decomposition techniques, upper and lower bounding (also across components), elaborate and dynamically chosen bid-ordering heuristics, and a host of structural observations. CABOB attempts to capture structure in any instance without making assumptions about the instance distribution. Experiments against the fastest prior algorithm, CPLEX 8.0, show that CABOB is often faster, seldom drastically slower, and in many cases drastically faster-especially in cases with structure. CABOB's search runs in linear space and has significantly better anytime performance than CPLEX.We also uncover interesting aspects of the problem itself. First, problems with short bids, which were hard for the first generation of specialized algorithms, are easy. Second, almost all of the CATS distributions are easy, and the run time is virtually unaffected by the number of goods. Third, we test several random restart strategies, showing that they do not help on this problem-the run-time distribution does not have a heavy tail.

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Towards a universal test suite for combinatorial auction algorithms

Cited by 252 publications

References 17 publications

Matrix Bidding in Combinatorial Auctions

Matrix Bidding in Combinatorial Auctions

Automatically improving the anytime behaviour of optimisation algorithms

CABOB: A Fast Optimal Algorithm for Winner Determination in Combinatorial Auctions

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