
Vasek Chvatal
Rutgers University
TSP cuts that do not follow the template paradigm
The first computer implementation of the
DantzigFulkersonJohnson cuttingplane method for solving the
traveling salesman problem, written by Martin, used subtour
inequalities as well as cutting planes of Gomory's type. The practice
of looking for and using cuts that match prescribed templates in
conjunction with Gomory cuts was continued in computer codes of
Miliotis, Land, and Fleischmann.
Grötschel, Padberg, and Hong
advocated a different policy, where the template paradigm is the only
source of cuts; furthermore, they argued for drawing the templates
exclusively from the set of linear inequalities that induce facets of
advocated a different policy, where the template paradigm is the only
source of cuts; furthermore, they argued for drawing the templates
exclusively from the set of linear inequalities that induce facets of
the TSP polytope.
These policies were adopted in the work of Crowder
and Padberg, in the work of Grötschel and Holland, and in the work
of Padberg and Rinaldi; their computer codes produced the most
impressive computational TSP successes of the nineteen eighties.
Eventually, the template paradigm became the standard frame of
reference for cutting planes in the TSP.
I will outline a technique for finding cuts that disdains all
understanding of the TSP polytope and bashes on regardless of all
prescribed templates. Combining this technique with the traditional
template approach in Concorde  the computer code written by David
Applegate, Bob Bixby, Bill Cook, and myself  was a crucial step in
our solution of a 13,509city TSP instance and a 15,112city TSP
instance.
William Cook
Princeton University
Optimization via Branch Decomposition
Robertson and Seymour introduced branchwidth as a new connectivity
invariant of graphs in their proof of the Wagner conjecture. Decompositions
based on this invariant provide a natural framework for implementing dynamic
programming algorithms to solve graph optimization problems. In earlier
work on the traveling salesman problem we used this framework in a heuristic
algorithm to obtain nearoptimal solutions to largescale instances.
In this
talk we will discuss the computational issues involved in using branchwidth
as as a general tool in discrete optimization. We will present applications
to euclidean steiner tree problems, graph bipartition, maximum cut problems,
and maximum stable set problems.
David Johnson
AT&T Research
Heuristics for the Traveling Salesman Problem: The State of the Art
During the last two years, I helped organize a "DIMACS
Implementation Challenge" on heuristics for the Traveling Salesman
Problem. Participants came from all over the world and included
representatives of all the top research groups currently active
in the field. They were asked to run their codes on a collection
of test instances of various types, ranging in size from 1,000 to
1,000,000 cities and report their results, along with times for a
benchmark code so that we could infer something about the relative
speeds of their machines. In this talk I will describe the
Challenge and identify the algorithmic approaches that worked best
for various choice of how much running time one is willing to spend
as a function of instance size. The range is from heuristics that
take little more time than that needed to read an instance and
still get within 50% of optimum to those that get within a few
percent of optimum for a 100,000 city in seconds to those that get
within a fraction of a percent of optimum for instances of this
size in a few hours. For more information, see the
Challenge website at http://www.research.att.com/~dsj/chtsp/.
George Nemhauser
Georgia Tech
A Polyhedral Study of the Cardinality Constrained Knapsack Problem
A cardinality constrained knapsack problem is a continuous knapsack
problem in which no more than a specified number of nonnegative variables
are allowed to be positive. This structure occurs, for example, in areas
such as finance,
location, and scheduling. Traditionally, cardinality constraints are
modeled by introducing auxiliary 01 variables and additional constraints
that relate the continuous and the 01 variables. We use an alternative
approach, in which we keep in the model only the continuous variables, and
we enforce the cardinality constraint through a specialized branching
scheme and the use of strong inequalities valid for the convex hull of the
feasible set in the space of the continuous variables. To derive the valid
inequalities, we extend the concepts of cover and cover inequality,
commonly used in 01 programming, to this class of problems, and we show
how cover inequalities can be lifted to derive facetdefining
inequalities. We present three families of nontrivial facetdefining
inequalities that are lifted cover
inequalities. Finally, we report computational results that demonstrate
the effectiveness of lifted cover inequalities
and the superiority of the approach of not introducing auxiliary 01
variables over the traditional MIP approach for this class of problems.
This is joint work with Ismael de Farias.
Bruce Shepherd
Lucent
Revisiting the LucchesiYounger Theorem
The LucchesiYounger Theorem
gives a minmax formula for the problem of
finding a mincost set of arcs in a digraph whose contraction
(or reversal, actually) results in a strongly connected graph.
This result, together with standard blocking theory,
imply that the problem of finding
such a set of arcs can be found in polytime via linear programming.
In 1981, Andras Frank gave a combinatorial algorithmic proof
of the result (whose running time was $O(n^5)$).
Gabow subsequently gave a different algorithm with running time $O(n^2m)$.
We recast
Frank's algorithm to achieve this faster running time but
using only standard tools of the trade:
ear decompositions, maximal arborescences, and negative cycle detection.
This is joint work with Adrian Vetta.
Optimization Roundtable
The next 10 years in Discrete Optimization
During the last ten years we have seen a steadily growing fundamental
development of Discrete Optimization, paralleled with an interest in effective
implementation of advanced algorithmic techniques, and also paralleled by a
very rapidly growing impact on practical applications. Topics that we will discuss
include: What are the major theoretical and computational thrusts likely to be
made during the next 10 years? Will optimization continue to assume a growing
importance in the business world? What areas of (discrete) optimization, and which
realworld applications are likely to receive the most attention?