An Algorithm for Optimal Winner Determination
in Combinatorial Auctions
Tuomas Sandholm
Computer Science Department, Carnegie Mellon University, 5000 Forbes
Avenue, Pittsburgh, PA 15213, USA
;
E

mail address:
sandholm@cs.cmu.edu
This material was first presented as an invited talk at the 1
st
International
Conference on Information and Computation Economics 10/28/1998, and
finally was released in
Artificial Intelligence 135 (2002) 1

54
Auction Mechanisms
Sequential Auction

Auction items one by one
1.
Easy for sellers (asking

side) to determine the winner
2.
Difficult for buyers (bidding

side) to win a collection of favorite items.
–
the
problem is that the market requirement cannot be fulfilled by this kind of
auction mechanisms.
Parallel Auction

Auction items at the same time in a period
1.
Easy for sellers to determine the winner
2.
Every buyers who want a collection of favorite items prefers to wait until the
end of the period to see what the going the prices on his favorite items will be
and then to optimize his bids so as to win all of his favorite items.
–
the
problem is that everybody may use this strategy!
Combinatorial Auction
–
Buyers bid a combination of items
1.
Very hard (NP) for sellers to determine the winner.
–
computation problem.
2.
Buyers may flexibly to bid what they want in a fair competition.
Computational Model of Combinatorial Auctions
A collection
of
m
items
Buyers (bidders)
•
Agent

1
•
Agent

2
•
……
•
Agent

i
•
……
•
Agent

n
A Seller
M
:: m = M
S
b
i
(S) > 0
b
i
(S) = 0 means the bidder i
does not bid on S.
b*
(S) = max
i
bidders
b
i
(S)
b*(S) = 0 means no any buyer bid on S.
P!
max
W
A
(
S
W
b*
(S))
W is a partition of M.
A is the set of exhaustive
partitions on M.
NP!
Objectives:
Maximize
sellers’ revenue
An Example of Exhaustive Partitions on 4 Items
M = {1, 2, 3, 4}
M = m = 4
(m
m/2
) <
q=1,2,…,m
Z(m, q) <
O(m
m
)
In other words, q is how many S in
one partition (or level)
max
W
A
(
S
W
b*
(S))
W
S
1
, S
2
, S
3
Solutions to the Winner Determination in
Combinatorial Auctions (NP

Complete)
Exhaustive Enumeration
When time or space is infinite, NP=P
Dynamic Programming
M.H. Rothkopf, A. Pekec, R.M. Harstad,
Computationally manageable
combinatorial auctions
, Management Sci. 44 (8) (1998) 1131
–
1147.
Significantly faster than exhaustive Enumeration
–
O(3
m
)/
(
2
m
)
Intractable
–
the key reason is this approach considered every
combination S in the exhaustive partitions even if the corresponding bid
does not happen in practice.
Ideas of The Polynomial Approximation Algorithm
1.
M.H. Rothkopf, A. Pekec, R.M. Harstad,
Computationally manageable
combinatorial auctions
, Management Sci. 44 (8) (1998) 1131
–
1147.
2.
Only consider “relevant bids” or “relevant partitions”
3.
Restrict the combinations (bids) based on some criteria:
•
At most 2 items in one bid
–
O(m
3
)
•
Use the tree structure to limit the bid forms
–
O(m
2
)
•
Bidding based on some ordering of items
•
Use families of combinations of items in which local winner
determination is very easy
4.
Find the approximate solution, which is close

to

optimal.
Computing
Speed
Economic
Efficiency
Trade

off
The New Optimal Search Algorithm
This paper’s contribution
The
input
(after only the highest bid is kept for every combination of items for which a bid
was received

all other bids are deleted) is
a list of bids
:
{B
1
, . . . ,B
n
} = {(B
1S
, B
1b*
), . . . ,
(B
nS
, B
nb*
)}
where B
jS
is the set of items in bid j, and B
jb*
is the price in bid j.
Auction Market
Bidding
1. Preprocessing
2. Construct Bid

tree
3. Tree

Search for the Solution
IDA* + Branch & Bound Search = SEARCH1 on the Tree
*Organize all the preprocessed bids into a tree:
Features of the Search

tree
: The path from the
root to a node (interior or leaf ) corresponds to a
relevant partition. In other words, each relevant
partition W
∈
A’ (relevant) is represented by
exactly one such path.
IDA* + Branch & Bound Search = SEARCH1 on the Tree
*Organize all the preprocessed bids into a tree:
IDA*
At any node, an upper bound on the
total revenue that can be obtained by
including that search node in the
solution is given by f = g + h(F).
The function h(F) gives an upper
bound on how much revenue the
items F that are not yet allocated on
the current search path can
contribute.
“g” is the sum of the prices of the
bids that are on the current search
path.
Branch & Bound Search (BBS)
After the first solution found, the
search switch to BBS for the optimal
solution.
Features of the Search

tree
: The path from the
root to a node (interior or leaf ) corresponds to a
relevant partition. In other words, each relevant
partition W
∈
A’ (relevant) is represented by
exactly one such path.
Complexity
: exponential in the number of items
but polynomial in the number of bids.
How to generate such a powerful tree?
Conditions of Tree Construction.
Every relevant partition
W
∈
A’ is represented in the tree by exactly one path from
the root to a node (interior or leaf )
if the children of a node
are those bids that:
•
include the item with the smallest index among the items
that have not been used on the path yet, and
•
do not include items that have already been used on the
path
The key is the generation of
children:
Naïve
:
Loop though the list of bids
–
O(n*m)
Bid

tree
:
In the worst

case, the
improvement is moderate.
In other cases, it improves
significantly greater. The best
one is O(1) and most of cases
(in the average case) are
O(max(m
–
log
n, n)
Secondary DFS (SEARCH2) on Bid

Tree
*Insert all the preprocessed bids into a bid

tree:
Usage of Stopmask
The Stopmask is a vector with one variable
for each item, Stopmask[i], where i
∈
M.
If Stopmask[i] = BLOCKED, SEARCH2 will
never progress left at depth i. This has the
effect that those bids that include item i are
pruned instantly and in place.
If Stopmask[i] = MUST, then SEARCH2
cannot progress right at depth i. This has the
effect that all other bids except those that
include item i are pruned instantly and in
place.
If Stopmask[i] = ANY corresponds to no
pruning based on item i : SEARCH2 may go
left or right at depth i .
i=1
i=2
i=3
M = {1, 2, 3}
IDA*+BBS
In the beginning, Stopmask[1] = MUST and
others Stopmask[i] = ANY.
The first child of any given node in the
search

tree is determined by a secondary DFS
from the top of the Bid

tree until a leaf (bid)
is reached.
Secondary DFS on Bid

Tree (Cont.)
*Insert all the preprocessed bids into a bid

tree:
When a bid B is reached, is appended to the
path of the search

tree and the algorithm sets
Stopmask[i] = BLOCKED for all i
∈
B.S and
Stopmask[i*] = MUST, where I* is the
minimum index at that time.
When backtracking a bid from the path of the
search

tree, all the MUST and BLOCKED
values are changed back to ANY, and the
MUST value is reallocated to the place in the
Stopmask where it was before that bid was
appended to the path.
The next unexplored sibling of any child, j, of
such a node in the search

tree is determined
by continuing the secondary

DFS. by
backtracking in the Bid

tree after the tree

search has explored the tree under B.
i=1
i=2
i=3
M = {1, 2, 3}
IDA*+BBS
In the beginning, Stopmask[1] = MUST and
others Stopmask[i] = ANY.
The first child of any given node in the
search

tree is determined by a secondary DFS
from the top of the Bid

tree until a leaf (bid)
is reached.
Preprocessing for Bid

tree Construction
PRE1: Keep only the highest bid for a combination
Inserting a bid into the Bid

tree is O(m) because insertion involves following or creating a
path of length m. There are n bids to insert. So, the overall time complexity of PRE1 is O(mn).
PRE2: Remove provably noncompetitive bids
A bid (
prunee
) is non

competitive if there is
some disjoint collection of subsets of that bid
such that the sum of the bid prices of the subsets
exceeds or equals the price of the prunee bid.
This picture is to show how to determine a new
bid on (1, 2, 3, 4) being non

competitive.
The tree is the search

tree which irrelevant bids
are removed.
Search on this tree to make decisions.
$10
$4
$7
Its complexity is similar to the IDA*+BB
if some bid contains all the items, because
it uses the search

tree.
PRE3: Decompose the set of bids into connected components
Preprocessing for Bid

tree Construction (Cont.)
The bids are divided into sets such that no item is shared by bids from different sets.
PRE4 and the tree

search are then done in each set of bids independently, and using
only items included in the bids of the set.
PRE4: Mark noncompetitive tuples of bids
Noncompetitive tuples of disjoint bids are
marked so that they need not be considered on
the same path in SEARCH1.
For example
, the pair of bids $5 for items (1,
3), and $4 for items (2, 4) is non

competitive if
there is a bid of $3 for items (1, 2) and a bid of
$7 for items (3, 4)
This is similar to PRE2. The difference is the
prunee is a virtual bid that contains the items
of the bids in the tuple.
The price of the virtual prunee is the sum of
the prices of those bids.
$3
$4
$7
$3
$3
Its complexity is similar to the IDA*+BB
if some bid contains all the items, because
it uses the search

tree.
Empirical Evaluation
Complexity
•
Polynomial in the number of bids
•
Exponential in the number of items
Four Different Bid Distributions
•
Random
: For each bid, pick the number of items randomly from 1, 2, . . .,m.
Randomly choose that many items without replacement. Pick the price randomly
from [0, 1].
•
Weighted random
: As above, but pick the price between 0 and the number of items
in the bid.
•
Uniform
: Draw the same number of randomly chosen items for each bid. Pick the
prices from [0, 1].
•
Decay
: Give the bid one random item. Then repeatedly add a new random item
with probability α until an item is not added or the bid includes all m items. Pick
the price between 0 and the number of items in the bid.
Problem instances (random
distribution) tended to be
easy because the bids were
long. So, the search path
was short.
In both the weighted and
the unweighted case, the
curves are sublinear
meaning that execution
time is polynomial in bids.
This is less clear in the
unweighted case.
The bulk of the execution time was spent
preprocessing rather than searching.
On all of the other distributions than
weighted random, the preprocessing time
was a small fraction of the search time.
The uniform distribution
was harder. The bids were
shorter so the search was
deeper.
The second figure shows
the complexity decrease
as the bids get longer, that
is, the search gets
shallower.
In practice, the auctioneer
usually can control the number
of items that are being sold in a
combinatorial auction, but
cannot control the number of
bids that end up being
submitted. Therefore, it is
particularly important that the
winner determination algorithm
scales up well to large numbers
of bids.
On the random and weighted
random distributions, the
algorithm scales very well to
large numbers of bids.
The uniform and decay
distributions were much harder.
However, even on these
distribution, the curves are
sublinear, meaning that
execution time is polynomial in
bids.
On the random and weighted random distributions, the algorithm scales very well to large
numbers of items. The curves are sublinear, meaning that execution time is polynomial in items.
The uniform and decay distributions were harder.
But, the curves are sublinear, meaning that
execution time is polynomial in items.
Conclusion
1.
Exhaustive enumeration and dynamic programming are not
practical.
2.
Limitation on the forms of bidding may sacrifice the
economic efficiency.
3.
The new algorithm is exponential in the number of items
and polynomial in the number of bidding, which fulfill the
market requirement much better by means of limiting on
the number of items.
4.
If the number of items are lot, as long as the sparseness of
bidding is good enough, this algorithm is still practical.

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