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OPTIMA

Mathematical Optimization Society Newsletter


Steve Wright
MOS Chair's Column


June 28, 2010. This is the first issue of Optima under our society's
new name, Mathematical Optimization Society. After a vigorous and
enlightening discussion, members approved the change by the deci-
sive margin of 457 to 127, with an additional 38 voters registering
an abstention. We are working through the many practical issues as-
sociated with the change, including a new web address mathopt.org,
which will come online soon, and a new logo. (Content of the web
site mathprog.org has already been updated at its current URL.) The
society will be moving ahead with renewed vigor under the new,
more descriptive name.
Andrea Lodi (Optima editor) and Alberto Caprara (co-editor)
have asked to resign their offices, following publication of the next is-
sue. We are most grateful for their excellent work during these past
three years in reviving Optima, following a moribund period. Under
their leadership, the format was revamped and the publication ar-
rangements and layout were changed. I am delighted to announce
that the other co-editor, Katya Scheinberg, has agreed to become
the new editor. I thank Katya for her service to date and her willing-
ness to continue in this new capacity. If you have any comments on
Optima, or ideas on format and feature articles, I am sure that Katya
would be happy to hear from you!
This is my last column as chair (though I will remain as vice-chair
for two more years). It has been a privilege to serve during these
past three years. Much has happened during that time: a wonderful
ISMP 2009 in Chicago, successful IPCO and ICCOPT conferences
and establishment of ICCOPT as a regular conference of the society,
inauguration of Mathematical Programming Computation, overhaul and
modernization of the constitution and by-laws, redesign and reimple-
mentation of the society's web site, revival of Optima, incorporation
of our membership directory in the online Combined Membership
Listing at www.ams.org/cml/, establishment of the Committee on
Stochastic Programming (COSP) as a technical section of the soci-
ety, and of course the change of name to MOS. Such ongoing activ-
ities as Mathematical Programming, Series A and B and Optimization
Online remain in good shape, and the bugs in the online archive of
the journals at Springer Online have been ironed out. Further chal-
lenges and opportunities remain. I hope you will work constructively
with incoming chair Philippe Toint (whose term begins on the first
anniversary of ISMP 2009) and the other officers and councilors on
the initiatives they undertake during the next term.
There are many ways that you can advance the mission of MOS -
refereeing, editing, and publishing in the journals; organizing the con-
ferences; serving on prize committees; running for office in MOS;
contributing to Optima and Optimization Online. I urge you all to


find a way to contribute your time and energy! MOS serves the
profession best when its members are engaged and active.
MOS's all-volunteer leadership model is one of its most appeal-
ing traits, but as our responsibilities grow, it puts more and more
of a burden on some officers. The new leadership should certainly
consider different models for the professional assistance that MOS
receives, to increase the effectiveness of officers and improve our
service and efficient use of revenues. Also on the table in the months
ahead will be a possible change to our membership model, tying at-
tendance at ISMP more closely to membership in MOS during the
three-year term following each symposium.
I close by wishing Philippe and the incoming team all the best. The
future for MOS and optimization is bright. How lucky we are to be
involved in the profession and the society at such an exciting time
for the field!




Contents of Issue 83 I July 2010
I Steve Wright, MOS Chair's Column
I James Ostrowski, Jeff Linderoth, Fabrizio Rossi and Stefano
Smriglio, Solving Steiner Triple Covering Problems
7 George Nemhauser, Vintage Steiner Triple System Paper
Revisited
8 Imprint





James Ostrowski, Jeff Linderoth, Fabrizio Rossi and Stefano Smriglio
Solving Steiner Triple Covering Problems


I Introduction

First, we supply data for two integer programming (set cover-
ing) problems which we believe are computationally hard. Our
experience indicates that optimal solutions to these problems
are very tedious to compute and verify, even though they have
far fewer variables than numerous solved problems in the liter-
ature.
Thus begins Fulkerson, Nemhauser, and Trotter's 1973 paper [9], in-
troducing two pure binary integer programs, sts27 and sts45, of
respectively 27 and 45 variables. These instances are Steiner Triple
Covering Problems, and at the time, Fulkerson, Nemhauser, and Trot-
ter were able to solve only the instance sts27. The instance sts45






OPTIMA 83


was not solved until many years later. Despite nearly 40 years of ex-
ponential growth in processing power, combined with even greater
advances in integer programming methodology, small instances in
the sts family still pose exceptional challenges to state-of-the-art
integer programming software.
The purpose of this note is to briefly describe the manner in
which we were able to prove the optimality to the two smallest
unsolved instances in the family, sts135 and sts243. The proce-
dure relied on symmetry-exploiting branching methodologies, enu-
meration, and the use of powerful high-throughput "cloud" com-
puting platforms. This note provides only some background on the
instances and an overview of the approach. The interested reader
may turn to [24] for further details.


I. Background
The sts integer programs are set covering problems generated from
Steiner Triple Systems. To understand a Steiner Triple System, con-
sider the Kirkman School Girl Problem, as it was proposed by Kirkman
in 1850 in the Lady's and Gentleman's Diary.
Fifteen young ladies in a school walk out three abreast for seven
days in succession; it is required to arrange them daily, so that
no two shall walk twice abreast.
A Steiner Triple System (STS) on a set, X, of n elements is a collec-
tion, B, of 3-sets (triples) such that for any two elements x and y in
X, the pair x and y appears in exactly one triple in B. A solution
to the Kirkman School Girl problem is a Steiner Triple System, as
each walking group consists of 3 girls, while each pair of girls are in
exactly one walking group together. The School Girl problem comes
with the additional restriction that the collection of I| = 35 triples
be divided into seven sets of five triples, one for each day, such that
each element, or girl, appears exactly once in the set of five triples
for that day.
Finding Steiner Triple Systems has interested mathematicians,
not to mention ladies and gentlemen, for years. In 1847, Kirkman
showed that a Steiner Triple System exists for a set X if and only if
either IX = 6k + 1 or IX = 6k + 3, for some positive integer k.
While there is only one non-isomorphic Steiner Triple System with
size 7 and 9, the number grows considerably after that. Remarkably,
there are more than 1010 many nonisomorphic triple systems with
size 19.
A Steiner Triple System for a set X can be represented by a (0, 1)-
matrix A that has a column for every element in X and a row for
every triple in B. The matrix element i,, 1 if and only if element
j is a member of triple i, and .1,, 0 otherwise.
The 1-width of a (0, 1) matrix A is the minimum number of
columns that can be selected from A such that all rows have at
least one 'I' in the selected columns. In other words, the 1-width
of a matrix A e {0, 1'mxn is the solution value of the following set
covering problem:

def
w(A) def min {lx | Ax > 1,, (I)
xj{0,1}n

where 11 is an --dimensional vector of ones. Fulkerson and Ryser
[8] studied the I-width of incidence matrices of Steiner Triple Sys-
tems and were able to show that the I-width w(An) > (n 1)/2
for all Steiner Triple incidence matrices An. They conjectured at the
time an upper bound on the incidence width of w(An) < 2n/3 1,
a bound that holds for all Steiner Triple Systems of size n < 15.
Later, Ryser constructed a 45 element system and speculated that
it had a 1-width of value 30, which would contradict their earlier
upper bound conjecture. This 45 element system is precisely the in-
stance sts45 that Fulkerson, Nemhauser, and Trotter introduced in


in [9]. Unable to solve sts45, Fulkerson, Nemhauser, and Trotter
demonstrated a Steiner Triple System of size 27, whose incidence
matrix A27 had w(A27) = 18, thus disproving the conjecture. This
is the sts27 instance.
The large 1-width Steiner Triple Systems of sizes 27 and 45 were
created by using a special tripling procedure on triples of sizes 9 and
15 respectively. This tripling procedure induces significant symme-
try.
If An e {0, 1}mxn is incidence matrix of a Steiner Triple System
(STS) of order n, then the tripled STS incidence matrix is


SAn

A3n =
I
D\ D


An
An ,
I I
D2 D3


where the matrices Di e {0, 1}6mx have exactly one "I" in every
row.
The tripling procedure applied to sts27, sts45, and sts81 gen-
erates problems with size 81, 135, and 243, respectively. The in-
stance sts81 was first solved by Mannino and Sassano [14] 15 years
ago, and it remained, until recently, the largest problem instance in
this family to be solved. The best known solutions to sts135 and
sts243 have values 103 and 198 respectively, and were both re-
ported by Odijk and van Maaren [20].
Fulkerson, Nemhauser, and Trotter give three explanations why
the sts instances may be especially challenging for integer program-
ming algorithms. First, the instances are highly symmetric. Unless the
symmetry is recognized and exploited, branch-and-bound algorithms
will require a prohibitive amount of enumeration. Second, the opti-
mal bases of the linear programming (LP) relaxations of the instances
have large determinants, making cutting plane methods based on
group-theoretic methods very difficult. Third, the instances have a
large integrality gap. Specifically, the optimal value of the LP relax-
ation for an instance of size n is n/3, while the optimal solution has
value approximately 2n/3. In the remainder of the section, we will
briefly provide some intuition and evidence for the first two of these
challenges.

1.2 Symmetry
To explain why branch-and-bound may require a prohibitive amount
of enumeration in the presence of symmetry, Fulkerson, Nemhauser,
and Trotter reference an interesting problem described by Jeroslow
[10]. A variant of Jeroslow's problem may be stated as

min {Xn+, I 2xi + 22 + ... + 2Xn + Xn+ = 2k + 1}
xC{0,1}n+l
where k e Z+, k < (3)

Jeroslow shows that branch-and-bound techniques that use linear
k
programming relaxations require solving at least 22 subproblems.
To see why symmetry is a major confounding issue in branch and
bound's ability to solve (3), consider the specific instance


min xs I 2 + 2x 2x2 3 + 2x4 + Xs = 3}.
xC{0,1}5


Figure I shows a branch-and-bound tree for solving (3). (Only sub-
problems for which the LP relaxation is feasible are included in the
figure). The LP relaxations of all non-leaf nodes in the tree have
optimal objective value 0. The optimal solution to each of the leaf
subproblems is also an optimal solution to the ILP, and hence each
has an objective value of I.






July 2010


Figure I. Enumeration Tree for jeroslow's problem


Consider node D, the subproblem formed by fixing xi to zero
and x2 to one. We can rewrite the subproblem as

min {xs I 2X3 + 2x4 +s X 1}. (5)
xe10,115

Now consider node E, the subproblem formed by fixing xl to zero
and x2 to one. This subproblem can also be written as

min {xs I 2x3 + 2X4 +s 1}. (6)
xeC0,1i5

The subproblem at node D is identical to the subproblem at node E.
Now it is easy to understand why traditional methods have difficulty
with this type of problem, as neither node D nor E are pruned even
though they are identical. It is obviously not necessary to consider
both nodes D and E, but traditional methods provide no way of rec-
ognizing that the two nodes represent equivalent problems. Further
inspection shows that there are many other nodes that represent
identical subproblems (for instance, nodes G, H, and I represent
the same subproblem, as do nodes L, M, N, and 0).
In Jeroslow's problem, a solution is feasible (and optimal) if and
only if x,,+ and k of the first n variables have value one (the rest
zero). Thus, given a feasible solution, permuting any of the first n
elements would yield a solution that is guaranteed to be feasible
and has the same objective function value. In this sense, the first n
variables are equivalent, inducing significant symmetry.
Effective symmetry-breaking techniques use symmetry to reduce
the region that is searched. A first strategy consists in adding (non-
valid) symmetry-breaking inequalities that keep in the feasible region
a representative solution (i.e. an equivalent solution) for each feasi-
ble solution removed. For example, consider the set of constraints
xl > X2 > ... > Xn in Jeroslow's problem. These constraints re-
move all but one solution from the feasible region. However, every
solution removed is equivalent to the one remaining feasible solu-
tion. With these constraints in place, the integer program (IP) can be
solved by a pure branch and bound algorithms with only 2 subprob-
k
lems, compared to the minimum 22 without symmetry-breaking.
For a general IP, the presence of large degrees of symmetry imply
that there will be large classes of equivalent solutions. Because of
these collections of equivalent solutions, it is likely that many of the
subproblems solved in the branch-and-bound trees will be equiva-
lent. Unless the equivalence is recognized, branch-and-bound trees
may be thousands of times larger than what is necessary, making
even the easiest problems impossible to solve.
In a series of papers, Margot [15, 16, 17] described an applica-
tion of isomorphism-free backtracking (known in the combinatorics


community) in the realm of IP Isomorphism pruning examines the
feasible region of each subproblem in the branch-and-bound tree
and can recognize when a subproblem to be solved would be equiv-
alent to another node in the search tree. A related idea, branching
on orbits of variables, was explored by the authors of this paper [23].
To formally describe symmetry in IP, let Fnl be the set of all per-
mutations of {1, 2,..., n}. This set (along with the binary operation
of composition) forms the complete symmetric group. Any subgroup
of the complete symmetric group is a permutation group. For a per-
mutation group F, the orbit of a point z under the action of the group
is orb(F,z) def {T(z) I Tr r}. The set orb(F,z) can be considered
as the collection of "equivalent" elements to z, with respect to the
permutations in F.
Let An E {0, 1}mxn be the incidence matrix of a Steiner Triple
System, and consider the collection of permutations

F {nr E n I 3T e Hm' such that PoAnPr = An},

where P, and Pr are the permutation matrices associated with o
and Tr respectively. If x is a feasible solution to the IP (I), then 7r(x)
is also a feasible solution of the same objective value. Thus, F is a
group of permutations that identify equivalent solutions to (I). So-
lutions x and r (x), for any Tr e F, are called isomorphic.
Identifying the symmetry group F of (I) can be accomplished by
using software such as nauty [19] or saucy [2] that computes the
isomorphism group of a related graph. Liberti [ 12] and Ostrowski et
al. [23, 12] describe the construction in more detail.
Applying symmetry-breaking techniques to the Jeroslow problem
leads to branch-and-bound trees whose size is at most linear in n.
Unfortunately, this dramatic speedup is not seen with the instances
in the sts family, although symmetry breaking techniques can de-
crease computation times by several orders of magnitude. For exam-
ple, the original solution by Mannino and Sassano of sts81 did not
use any symmetry-breaking techniques and required solving almost
one billion subproblems (35 million for a processed problem). Mar-
got's implementation of isomorphism pruning required solver fewer
than 1,000 subproblems. However, symmetry breaking alone has not
led to the solution of larger sts instances.
This has been only a cursory explanation of symmetry in integer
programming. The reader interested in more details may see the the
recent survey of Margot [18].

1.3 Cutting Planes
Cutting plane methods for general integer programs have been dra-
matically improved in the last decade and very effective practical im-
plementations are now available in commercial solvers. Several types
of general purpose cutting planes are known. Among them, Gomory
fractional and mixed integer cuts, as well as disjunctive cuts, are often ef-
fective in closing the integrality gap, and help speed-up the solution
of many integer programming problems. However, this is not the
case of sts problems, as witnessed by several recent experimental
findings which are summarized below.
Fischetti and Lodi [6] performed an exact optimization over the
first Chvatal closure of the natural {0, 1}-LP formulation of sts27 and
sts45. This amounts to generating rank-I Chvatal-Gomory cuts un-
til one such violated cut exists. Their experiments show that one
gets no improvement with respect to the initial lower bound. The
same negative result has been obtained in [I] by optimizing exactly
over the split closure, that is, generating rank-I split cuts, which are
equivalent to Gomory mixed integer cuts.
Furthermore, Balas et al. [3] showed that a standard implementa-
tion of the Gomory's fractional cutting plane method (in which frac-
tional Gomory cuts of any rank are generated) is able to close 50%






OPTIMA 83


of the initial gap of stsl5, but it fails to improve the initial bound
when applied to sts27. Interestingly, they also demonstrated the
interesting result that an advanced implementation of the method
allowed to solve to optimality for the first time (with a pure cutting
plane algorithm) stsl5 and sts27.
As far as disjunctive cuts are concerned, they also prove to be
ineffective when applied to Steiner triple covering problems. In fact,
Fischetti and Lodi [7] showed that no improvement of the initial gap
has been accomplished on sts27 with ten rounds of disjunctive cuts.
This lack of improvement is quite striking if compared to what hap-
pens for most of the benchmark problems considered by the integer
programming community.
In our initial attempts to solve sts135 and sts243, we thought
an effective methodology may be to enumerate an isomorphism-
free tree of subproblems, down to the point where the nodes did
not contain any remaining symmetry. (The symmetry group F can
be recomputed at each node and generally gets significantly smaller
with each fixing of a variable). Then, we would generate a strong
relaxation for each of the isomorphism-free subproblems via a clo-
sure procedure, and attempted to solve each of the strengthened
subproblems.
We attempted this solution approach on sts135, running the
modified orbital branching code [23] to write the nodal subprob-
lem description to an MPS file and to fathom the node whenever
a node with no symmetry remaining was reached. This generated a
collection of 155 integer programs, and if we were able to solve each
of these instances, then we could claim that we had solved stsl35.
We sent one of these instances to Anureet Saxena, the author of a
code for computing the split closure of an integer program. Dr. Sax-
ena kindly attempted to solve this instance for us by first tightening
the relaxation via split cuts, and then giving the tightened instance
to CPLEX. Generating split cuts improved the lower bound from
the initial root relaxation value of 45 to the value 87.2 in 2.5 hours.
CPLEX was then able to solve the strengthened instance in 14 days
of CPU time, finding a solution of value 104, which was the optimal
solution that could be found in that portion of the subtree. As we
conjectured that the subproblem we gave to Dr. Saxena was one of
the easier of the 155 instance we needed to solve, we abandoned
this solution approach.
The lower bound after applying split cuts for the node given to Dr.
Saxena was improved to 87.2. However, there is a trivial observation
that can improve the lower bound at the root node to a value of 90.
A quick examination of the matrix A3n in (2) shows that the optimal
cover must contain at least 3 xw (An) elements, as each of the three
An must be independently covered. For example, since the first n
columns of a solution to sts(m = 3n) must also be a solution for
sts(n), the inequality ATx > w(An), with An def [In,0m-n] is
valid for sts(3n). Similarly, since the tripling construction process


is recursive, the inequality Ahx > w(An) is valid for sts(9n). This
simple insight, and using this insight to generate strong branching
disjunctions that exploit symmetry, is a specific case of the constraint
orbital branching technique of the authors [22], which is briefly de-
scribed in the next section.



2 Constraint Orbital Branching
Constraint orbital branching, is a branching method designed to ex-
ploit symmetry in integer programs. Given a constraint aTx < b
with (a, b) e In+l, the method is based on the fact that either an
equivalent form of aTx < b holds for one of the elements in the
inequality's orbit (orb(F, a)), or the inequality aTx > b + 1 holds
for all elements in the orbit. This simple observation suggests the
branching disjunction:


(acx < b) v A d Tx > b + 1
\deorb(r,a)


In the context of solving the stsl35, branching on (A45, w(A45))
implies that either there is a solution to stsl35 having exactly 30
of the first 45 components of value I, or for each of sets of indices
{1,45}, 46,90}, 91,135} at least 31 components must be I. This
improves the lower bound on the "right" branch of the branching
tree to 3 x 31 93.
To solve (I), we apply the disjunction (7) on constraints ob-
tained from the optimal solution to smaller embedded sts instances.
Specifically, we branch either on the constraints ATx < k, where
k e {w(Av), w(Av) + 1,... or on the constraints AT3x < q,
where q e {w(Av/3),w(Av/3) + 1...}.
Figure 2 shows the branching tree obtained by applying this
methodology to stsl35. The constraints added on the right branch
of the tree (like p'x > 31 Vp e orb(F,A)) greatly improve the
lower bound obtained by solving the linear programming relaxation.
In fact, the nodes D, F, and G in Figure 2 are pruned by bound, as the
value of the linear programming relaxation at these nodes exceeds
102, and a solution of value 103 is known.
Figure 3 shows the branching tree for solving sts243. In this case,
the nodes C, E, and H are pruned by bound.



3 Solution by Enumeration
Based on the bounds obtained after applying the orbital branching
disjunction, in order to solve stsl35, we must only devise a proce-
dure for processing the nodes A, B, C, and E of the branching tree
of Figure 2. Likewise, to solve sts243, only the nodes A, B, D, F,
and G of the branching tree of Figure 3 need be processed.


AT X < 30
45


Vp orb(rF,A45)
djTx >33


Vp orb(rF,A15)
px > 10


Vp orb(rF,A15)
jTX > 12


Figure 2. Branching Tree for Solution of stsl35






July 2010


A x < 61


Figure 3. Branching Tree for Solution of sts243


One mechanism for processing the nodes would be to solve them
with a black-box or commercial IP solver. Processing the nodes in
this fashion did not appear to be computationally feasible. For in-
stance, We ran CPLEX on the instance from node A in Figure 2 for
many days, and there was still a quite significant optimality gap.
An alternative for processing the nodes is based on enumer-
ation. By the construction of sts(3v), for any feasible solution
x* [x*, X2*,x3* ] with AX* < k, the first v components of
the solution, x1* e {0, 1}, must be a feasible solution to sts(v)
with cardinality at most k. Note that in the branching trees used for
solving sts135 and sts243, the first branch enforces the constraint
with k = w (A45) and k = w (Asi), respectively. So, for x* to be fea-
sible in that branch, x1* must be an optimal solution to sts45 or
sts81. The branching tree continues by incrementing k in ATx < k,
or in some cases by branching on AT3x < q.
Based on this observation, one technique for processing a node
in the tree defined by the branching inequality A ,x* < k is to enu-
merate all solutions {y, y2,... yT} to sts(v) of value k. Then, for
each solution h = 1,...,T, fix the first v variables of sts(3v) to
yh and solve the smaller IP. The best solution in the original node
is the best solution found among the T smaller integer programs.
This procedure is only likely to be effective if the smaller integer
programs are significantly easier than the original integer program,
and if the number of enumerated solutions T is not too large. Our
preliminary computational experience with this method indicated
that the reduced integer programs were often quite easily solved by
commercial IP software or by our orbital branching code. Also, to
reduce the size of T, we may again exploit symmetry, as it suffices
only to enumerate all non-isomorphic solutions to sts(v) of value k.
In our context, an advantage of this solution procedure enu-
merating partial solutions, fixing variables based on these partial so-
lutions, and solving the resulting subproblems is that the smaller
integer programs may be solved completely independently from one
another. This makes the procedure an ideal candidate for the use
of parallel computing. In general, with the advent of massively par-
allel processors, like the Jaguar system at Oak Ridge containing
more than 200,000 cores, (structured) partial enumeration may give
a mechanism for effectively using such high-performance modern
computing architectures to solve integer programs. In our work,
to provide the computational power for the solution of the stsl3 5
and sts243 instances in this manner, we used CPU cycles from a dis-
tributed collection of workstations provided by the Condor software
system [13]. Condor is a job scheduling software especially useful
for completing jobs in a high-throughput manner; that is, jobs that re-
quire large amounts of processing over long periods of time, like the
integer programs required to solve sts135 and sts243. Condor
has mechanisms to effectively harness idle CPU cycles from oth-


erwise desktop workstations, effectively creating a "computational
cloud" on which our integer programs were run. For a listing and de-
scription of the many features of Condor that make it a useful high-
throughput computing toolkit, the reader is referred to the Condor
web site (www.cs.wisc.edu/condor).



4 Solving stsl35 and sts243
Tables I and 2 detail the historical progress made in finding high-
quality feasible solutions for sts135 and sts243.


Table I. Best Solutions Reported for sts135
Author Value Date
Karmarkar et al. [I I] 105 1991
Feo and Resende [5] 104 1995
Mannino and Sassano [14] 104 1995
Odijk and van Maaren [20] 103 1998


Table 2. Best Solutions Reported for sts243
Author Value Date
Feo and Resende [4] 204 1989
Feo and Resende [5] 203 1995
Mannino and Sassano [14] 202 1995
Odijk and van Maaren [20] 198 1998


4.1 stsl35
In the sts135 branching tree shown in Figure 2, the nodes D, F,
and G all have a lower bound obtained by solving the LP relaxation
of value at least 103, so only nodes A, B, C, and E need to be pro-
cessed. The first step was to enumerate all solutions of stsl35
having solution value at most 33. This step was accomplished with
a "flexible" variant of an isomorphism pruning code [21], requiring
662 seconds on a 2.4GHz Intel Xeon processor. Next, the code was
run again to enumerate all solutions to sts45 of value 33 that also
obeyed the appropriate permutations of ATsx > 10. (See node E in
Figure 2). These constraints enforce that each of the three blocks of
15 variables in sts45 must have at least 10 ones. This enumeration
step required 4,894 seconds, 693,692 nodes, and produced 16,849
solutions. To solve the 2,080 + 16, 849 = 18, 929 integer programs
necessary to prove the optimality of the solution of value 103 to
sts135, the orbital branching code of Ostrowski et al. [24] was
used. An upper bound of value 103.1 was used to prune the branch
and bound tree. Note that since the objective function may take
only integer values for feasible solutions, a bound of 102 could have
been used, but 103.1 was used to ensure that the methodology and






OPTIMA 83


software was able to reproduce the best known solution. The inte-
ger programs were solved on a pool of heterogeneous workstations
managed by Condor at the University of Wisconsin-Madison.
Table 3 contains a summary of the computation, listing for each
node of the branch and bound tree of Figure 2, the number of non-
isomorphic solutions generated (also the number of integer pro-
grams that need to be solved to process that node), the total num-
ber of nodes evaluated in all the branch-and-bound trees, and the
total CPU time required to solve all the integer programs. All told,
slightly more than ten million CPU seconds (or roughly 126 CPU
days), were required for the computation. However, since the inte-
ger programs were solved in parallel, the total wall clock time was
20 hours and 19 minutes. The maximum CPU time required for any
one individual IP instance was 7,139 seconds. One solution of value
103 was found, and this solution was isomorphic to the one re-
ported by Odijk and van Maaren. This computation establishes that
the optimal solution to stsl35 has value 103.

Table 3. Statistics for sts135 IP Computations
Node # Sol Nodes CPU Time
A I 10,041 13m 37s
B 56 2,738,242 2d 2h 26m 8s
C 2,023 40,634,479 30d 9h 41m 40s
D Zroot = 105
E 16,849 114,346,449 93d 16h 52m Os
F root = 103
G Zroot = 108


Table 4. Statistics for sts243 IP Computations
Node # Sol Nodes CPU Time
A I 33,575 17h 47m 31s
B I 46,145 Id 4h 10m 25s
C zroot = 198
D 2 2,428 2h 38m 58s
E Zroot = 199
F N/A 379 Im 27s
G N/A 59 Im 5Is
H Zroot = 198


4.2 sts243
Odijk and van Maaren [20] have reported a solution of value 198
to the instance sts243, and based on the structure of the solution,
they conjecture the solution to be optimal. We are able to confirm
that the optimal solution does have value 198. In the sts243 branch-
ing tree shown in Figure 3, the nodes C, E, and H are all pruned by
bound. To process nodes F and G, we ran our orbital branching code
on the integer programs without enumerating solutions to sts81 and
fixing variables in sts243. A bound of 198.1 was used for pruning
the branch and bound tree, and processing both nodes required just
under 24 minutes, as detailed in Table 4.
To process nodes A, B, D, the enumerate-and-fix procedure de-
scribed in Section 3 was employed. All non-isomorphic solutions
to sts81 of value at most 63 were enumerated. This required 1,021
seconds and 2,420 nodes of the enumeration tree. Only 4 such solu-
tions were found. To solve the 4 integer programs to process these
nodes, the flexible isomorphism pruning code of Ostrowski, Lin-
deroth, and Margot was used [21 ], and an upper bound of 198.1 was
used for pruning. A summary of the computation is given in Table 4.
All 4 integer programs were solved on a Intel Core 2 CPU, clocked
at 2.4 GHz. The total CPU time required for the entire computation,
including enumeration was just over 51 hours.
Two solutions of value 198 were found, but they were both iso-
morphic to the solution reported by Odijk and van Maaren. Thus,


the optimal solution to sts243 has value 198. It is interesting that
(likely due to the high quality of the solution of value 198) signifi-
cantly less CPU effort was required to solve sts243 than the smaller
instance sts135.


5 Solutions for sts729
Finding high quality solutions to Steiner Triple Covering Problems
appears to also be quite difficult. As evidence, consider Tables I
and 2 detailing the best solutions to sts135 and sts243 reported
in the literature. Only the heuristic of Odijk and van Maaren, which
is based on the GSAT heuristic of Selman et al. [25] was able to find
the optimal solutions. However, the enumerate-and-fix procedure
described in Section 3 used to prove the optimality of the Odijk and
van Maaren's solutions to sts135 and sts243 was also able to (rel-
atively) easily find the best known solutions as well. This suggests
that the enumerate-and-fix procedure might also be effective as a
heuristic.
Thus, we attempted to find a high quality solution to the sts729
instance obtained by tripling sts243. We ran CPLEX (v9) on the
sts729 instance for more than two weeks, and the best solution
found had value 653. This solution is likely far from optimal, as a so-
lution of value 639 to sts729 can be obtained by taking the optimal
solution of sts243 of value 198, doubling it, and then adding I's in
the last 243 positions.
To test the effectiveness of enumerate-and-fix as a heuristic pro-
cedure, we enumerated 35,861 unique solutions of sts243 having
value at most 202. This enumeration process required over 10 mil-
lion branch and bound nodes using the orbital branching code of Os-
trowski et al. For 4,533 randomly chosen solutions, the first 243 vari-
ables of sts729 were fixed according to the solution, and CPLEX
12.1 was run for a maximum CPU time of 4 hours. All computations
were done using the same Condor computational grid used for the
solution of sts135 and sts243. The best solution obtained in this
manner had value 619, significantly better than could be found using
CPLEX.


6 Conclusions
We have been able to prove the optimality of solutions for two new
Steiner Triple Covering Problem, with systems of order 135 and
243. In both cases, the solution reported by Odijk and van Maaren
[20] was found to be an optimal solution. The instances were solved
by a combination of symmetry-exploiting branching methodology,
the enumeration of solutions to embedded subproblems, and paral-
lel "high-throughput" computing.
We strongly believe that the use of challenge problems for the
research community is an extremely important both to mea-
sure the progress of the field and in generating "next generation"
ideas that push the field in new directions. We have submitted
instances sts405 and sts729 for consideration into MIPLIB2010
(http://miplib.zib.de/miplib2010/), and we hope these instances will
spur the computational integer programming community to develop
new solution methodologies.

James Ostrowski, Department of Management Sciences, University of Wa-
terloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G I,
Canada. jostrow@engmail.uwaterloo.ca
Jeff Linderoth, Department of Industrial and Systems Engineering, University
of Wisconsin-Madison, 1513 University Avenue, Madison, WI 53706, USA.
linderoth@wisc.edu
Fabrizio Rossi, Dipartimento di Informatica, Universita di L'Aquila, Via Ve-
toio, 1-67010 Coppito (AQ), Italy. fabrizio.rossi@univaq.it
Stefano Smriglio, Dipartimento di Informatica, Universita di L'Aquila, Via Ve-
toio, 1-67010 Coppito (AQ), Italy. stefano.smriglio@univaq.it







July 2010


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Discussion Column

George Nemhauser

Vintage Steiner Triple System Paper

Revisited

To accompany the paper by Ostrowski, Linderoth, Rossi, and Sm-
riglio, Andrea Lodi has asked me to provide some background on
the original Steiner Triple system paper by Ray Fulkerson, Les Trot-
ter and me. This work was done at the School of Operations Re-
search and Industrial Engineering at Cornell. Ray Fulkerson joined


the department from the Rand Corporation in 1972 as a chaired
professor. Ray had been part of the fantastic group of mathemati-
cians along with Richard Bellman, George Dantzig, Les Ford, Phil
Wolfe and others who pioneered the development of modern opti-
mization including linear and nonlinear programming, dynamic pro-
gramming and network flows. I believe that Ray was one of the last
of these great men to leave Rand during the period when Rand was
eliminating its basic research program in mathematics and operations
research.
Soon after Ray arrived at Cornell, we had a discussion in which I
told him that I was interested in computational integer programming.
Ray told me that he and Herb Ryser had been studying a set cov-
ering problem regarding a conjecture about the I-width of Steiner
triple systems. He said, modest as always, that although he hadn't
been keeping up with computational techniques in integer program-
ming, he thought this was a particularly hard integer program for its
size. I asked him to tell me about it. At this point he took a large
piece of graph paper from his desk drawer that consisted of several
8x1 I sheets pasted together. It contained a handwritten 330 x 45 0-
I matrix (only the ones were shown) with precisely 3 ones in each
row. This is the A matrix for the set covering problem with an ob-
jective function of all ones, now known as Stein 45. The I-width of
the system is the optimal value.
I told Ray that I had a very bright graduate student by the name
of Les Trotter and suggested that the three of us work on trying
to solve Stein 45. As part of his Master's thesis, Les had developed
a branch-and-bound algorithm with linear programming relaxations
for solving binary integer programs. We also tried a pure cutting
plane algorithm using Gomory cuts. Remember, in the mid 1970s,
the idea, or at least the implementation, of combining cutting planes
with search had not yet emerged.
I won't go into details here but we were not able to solve Stein
45, although we solved a smaller triple system Stein 27 (117 rows
and 27 columns) that we constructed from Stein 45 and its solu-
tion resolved the conjecture. All of our computational success came
from the branch-and-bound algorithm; the cutting plane algorithm
could not solve a 15 element triple system with 35 rows and 15
columns. In our paper, we pointed out three main difficulties in solv-
ing these Stein instances: symmetry, very weak LP bounds, and very
large determinants associated with the bases of the optimal solutions
to the LP relaxations, which limit the effectiveness of Gomory cuts.
Great progress has been made in the last two decades in tighten-
ing LP bounds and implementing Gomory cuts. However, symmetry
remains a huge challenge. Recent works, such as the paper in this
issue and earlier seminal work by Francois Margot, give new ideas
for dealing with symmetry.
The Stein history has an interesting connection with my current
university and department, Industrial and Systems Engineering at
Georgia Tech. Besides my being a professor at Tech, Les Trotter
did his Masters degree at Tech and the branch-and-bound algorithm
we used came from his Masters thesis. Don Ratliff, now a colleague
at Tech, was, I believe, the first person to solve Stein 45 about five
years after we proposed it and soon after he arrived at Tech. Jeff
Linderoth, one of the co-authors of the Stein paper in this issue, is
a Tech PhD.
Finally, let me close with a story about a failed attempt to solve
Stein 45. The optimal solution to the LP relaxations of a Stein in-
stance has all of the variables equal to 3 and hence an optimal value
of -, where n is the number of variables. However, the optimal IP
value is at least 1 and can even be larger than ". Ryser spec-
ulated that the optimal value of Stein 45 was 30, and we believed it
was correct. Moreover, we had a lower bound of 28, but we were
reasonably sure that 28 was not feasible.






OPTIMA 83


In those days, the use of integer programming algorithms that
could find provably optimal solutions was extremely limited in prac-
tice. Practitioners and academics doing applied operations research
primarily used heuristics and there was somewhat of a "cult" built
up around people who had proprietary heuristics that supposedly
performed much better than rigorous search or cutting plane algo-
rithms. I happened to be at the University of Waterloo visiting Jack
Edmonds when one of these heuristic gurus was also there. teaching
a course on applied OR. I showed him Stein 45 and invited him to
see how good a solution he could find because the best we could
find was 30. A couple of hours later he told me that he could find
a solution of value 27. When I told him that was impossible he said
that surely he could find a solution of value 28. The next day I asked
if he had found a solution of value 28 and he replied that he had
lost interest since this was not a practical problem and there were
no consulting fees to be had. So I suggested a small bet of $100 to
make it interesting. He would win if he could find a solution of 28 in
a week. He said he couldn't afford the time for such a small amount
of money. So I said, how about $1000. He said that would be more
interesting, but was unfair since he was a rich consultant and I was
only a poor academic. Yes I said, I might be poor, but not dumb. So
let's make it for $10,000 and you will have my certified check in an
hour for that amount and you simply need to write personal check
given your wealth. Suddenly, the braggadocio disappeared. Five years
later Don Ratliff proved that 30 is indeed the optimal value.

D. R. Fulkerson. G. L. Nemhauser and L. E. Trotter, Jr., Two computationally
difficult set covering problems that arise in computing the I-width of inci-
dence matrices of Steiner triple systems, Mathematical Programming Study
2, 72-81, 1974.
George Nemhauser, Industrial and Systems Engineering, Georgia Institute of
Technology, Atlanta GA 30332 george.nemhauser@isye.gatech.edu


Toulouse Global Optimization Workshop

2010 (TOGO 10)

Celebrating Pierre Hansen's 70th Birthday

31 August 3 September 2010
ENSEEIHT 2 rue Camichel, 31071 Toulouse, France

Global Optimization is a discipline aimed at finding global optima
for hard nonconvex optimization problems. The workshop aims at
bringing together researchers dealing with this topic. Depending on
the number of submissions, we shall have either a single stream or
two/three parallel streams. Accordingly, only a limited number of
contributions will be accepted. A special issue of the Journal Of
Global Optimization will contain (fully refereed) papers derived from
works presented at the workshop (or on close topics).

Invited Speakers
Pierre Hansen (GERAD and HEC, Montr6al, Canada), Emilio Carri-
zosa (Universidad de Sevilla, Spain), Charles Audet (Ecole Polytech-
nique, Montr6al, Canada), Pietro Belotti (Lehigh University, USA).

Registration Fee
300 EUR (200 EUR for students). This fee includes entry to all tech-
nical sessions, lunches, coffee breaks, welcome cocktail, a social din-
ner and one copy of the conference program and abstracts.

Information and Contact
http://www.lix.polytechnique.fr/togo 10
gow.togo I @gmail.com


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IMPRINT
Editor: Andrea Lodi, DEIS University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy. andrea.lodi@unibo.it Co-Editors: Alberto Caprara, DEIS
University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy. alberto.caprara@unibo.it Katya Scheinberg, Department of Industrial and Systems Engineering,
Lehigh University, Bethlehem, PA. katyascheinberg@gmail.com Founding Editor: Donald W. Hearn Published by the Mathematical Optimization Society.
* Design and typesetting by Christoph Eyrich, Berlin, Germany. optima@0x45.de
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