In
mathematical optimization, the ellipsoid method is an
iterative method for
minimizing convex functions. When specialized to solving feasible
linear optimization problems with rational data, the ellipsoid method is an
algorithm which finds an optimal solution in a number of steps that is polynomial in the input size.
The ellipsoid method generates a sequence of
ellipsoids whose volume uniformly decreases at every step, thus enclosing a minimizer of a
convex function.
History
The ellipsoid method has a long history. As an
iterative method, a preliminary version was introduced by
Naum Z. Shor. In 1972, an
approximation algorithm for real
convex minimization was studied by
Arkadi Nemirovski
Arkadi Nemirovski (born March 14, 1947) is a professor at the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. He has been a leader in continuous optimization and is best known for his work ...
and David B. Yudin (Judin). As an algorithm for solving
linear programming problems with rational data, the ellipsoid algorithm was studied by
Leonid Khachiyan
Leonid Genrikhovich Khachiyan (; russian: Леони́д Ге́нрихович Хачия́н; May 3, 1952April 29, 2005) was a Soviet and American mathematician and computer scientist.
He was most famous for his ellipsoid algorithm (1979) for ...
; Khachiyan's achievement was to prove the
polynomial-time solvability of linear programs. This was a notable step from a theoretical perspective: The standard algorithm for solving linear problems at the time was the
simplex algorithm
In mathematical optimization, Dantzig's simplex algorithm (or simplex method) is a popular algorithm for linear programming.
The name of the algorithm is derived from the concept of a simplex and was suggested by T. S. Motzkin. Simplices are n ...
, which has a run time that ''typically'' is linear in the size of the problem, but for which examples exist for which it is ''exponential'' in the size of the problem. As such, having an algorithm that is ''guaranteed'' to be polynomial for all cases seemed like a theoretical breakthrough.
Khachiyan's work showed, for the first time, that there can be algorithms for solving linear programs whose runtime can be proven to be polynomial. In practice, however, the algorithm is fairly slow and of little practical interest, though it provided inspiration for later work that turned out to be of much greater practical use. Specifically,
Karmarkar's algorithm, an
interior-point method, is much faster than the ellipsoid method in practice. Karmarkar's algorithm is also faster in the worst case.
The ellipsoidal algorithm allows
complexity theorists to achieve (worst-case) bounds that depend on the dimension of the problem and on the size of the data, but not on the number of rows, so it remained important in
combinatorial optimization
Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, where the set of feasible solutions is discrete or can be reduced to a discrete set. Typical combi ...
theory for many years.
[V. Chandru and M.R.Rao, Integer Programming, Chapter 32 in '' Algorithms and Theory of Computation Handbook'', edited by M.J.Atallah, CRC Press 1999, 32-1 to 32-45.] Only in the 21st century have interior-point algorithms with similar complexity properties appeared.
Description
A convex minimization problem consists of the following ingredients.
* A
convex function to be minimized over the vector ''
'' (containing ''n'' variables);
* Convex inequality constraints of the form
, where the functions
are convex; these constraints define a
convex set ''
''.
* Linear equality constraints of the form
.
We are also given an initial
ellipsoid defined as
:
containing a minimizer
, where
and
is the center of
.
Finally, we require the existence of a
separation oracle for the convex set ''
. ''Given a point
, the oracle should return one of two answers:
* "The point
is in
", or -
* "The point
is in not in
, and moreover, here is a hyperplane that separates
from
", that is, a vector
such that
for all
.
The output of the ellipsoid method is either:
* Any point in the polytope ''
'' (i.e., any feasible point), or -
* A proof that ''
'' is empty.
Inequality-constrained minimization of a function that is zero everywhere corresponds to the problem of simply identifying any feasible point. It turns out that any linear programming problem can be reduced to a linear feasibility problem (e.g. minimize the zero function subject to some linear inequality and equality constraints). One way to do this is by combining the primal and dual linear programs together into one program, and adding the additional (linear) constraint that the value of the primal solution is
no worse than the value of the dual solution. Another way is to treat the objective of the linear program as an additional constraint, and use binary search to find the optimum value.
Unconstrained minimization
At the ''k''-th iteration of the algorithm, we have a point
at the center of an ellipsoid
:
We query the cutting-plane oracle to obtain a vector
such that
:
We therefore conclude that
:
We set
to be the ellipsoid of minimal volume containing the half-ellipsoid described above and compute
. The update is given by
:
where
:
The stopping criterion is given by the property that
:
Inequality-constrained minimization
At the ''k''-th iteration of the algorithm for constrained minimization, we have a point
at the center of an ellipsoid
as before. We also must maintain a list of values
recording the smallest objective value of feasible iterates so far. Depending on whether or not the point
is feasible, we perform one of two tasks:
*If
is feasible, perform essentially the same update as in the unconstrained case, by choosing a subgradient
that satisfies
::
*If
is infeasible and violates the ''j''-th constraint, update the ellipsoid with a feasibility cut. Our feasibility cut may be a subgradient
of
which must satisfy
::
for all feasible ''z''.
Performance
The ellipsoid method is used on low-dimensional problems, such as planar location problems, where it is
numerically stable
In the mathematical subfield of numerical analysis, numerical stability is a generally desirable property of numerical algorithms. The precise definition of stability depends on the context. One is numerical linear algebra and the other is algorit ...
. On even "small"-sized problems, it suffers from numerical instability and poor performance in practice .
However, the ellipsoid method is an important theoretical technique in
combinatorial optimization
Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, where the set of feasible solutions is discrete or can be reduced to a discrete set. Typical combi ...
. In
computational complexity theory, the ellipsoid algorithm is attractive because its complexity depends on the number of columns and the digital size of the coefficients, but not on the number of rows. In the 21st century,
interior-point algorithms with similar properties have appeared .
Notes
Further reading
* Dmitris Alevras and Manfred W. Padberg, ''Linear Optimization and Extensions: Problems and Extensions'', Universitext, Springer-Verlag, 2001. (Problems from Padberg with solutions.)
* V. Chandru and M.R.Rao, Linear Programming, Chapter 31 in ''Algorithms and Theory of Computation Handbook'', edited by M.J.Atallah, CRC Press 1999, 31-1 to 31-37.
* V. Chandru and M.R.Rao, Integer Programming, Chapter 32 in '' Algorithms and Theory of Computation Handbook'', edited by M.J.Atallah, CRC Press 1999, 32-1 to 32-45.
*
George B. Dantzig
George Bernard Dantzig (; November 8, 1914 – May 13, 2005) was an American mathematical scientist who made contributions to industrial engineering, operations research, computer science, economics, and statistics.
Dantzig is known for his ...
and Mukund N. Thapa. 1997. ''Linear programming 1: Introduction''. Springer-Verlag.
*
George B. Dantzig
George Bernard Dantzig (; November 8, 1914 – May 13, 2005) was an American mathematical scientist who made contributions to industrial engineering, operations research, computer science, economics, and statistics.
Dantzig is known for his ...
and Mukund N. Thapa. 2003. ''Linear Programming 2: Theory and Extensions''. Springer-Verlag.
*
L. Lovász: ''An Algorithmic Theory of Numbers, Graphs, and Convexity'', CBMS-NSF Regional Conference Series in Applied Mathematics 50, SIAM, Philadelphia, Pennsylvania, 1986
* Kattta G. Murty, ''Linear Programming'', Wiley, 1983.
*
M. Padberg, ''Linear Optimization and Extensions'', Second Edition, Springer-Verlag, 1999.
*
Christos H. Papadimitriou
Christos Charilaos Papadimitriou ( el, Χρήστος Χαρίλαος "Χρίστος" Παπαδημητρίου; born August 16, 1949) is a Greek theoretical computer scientist and the Donovan Family Professor of Computer Science at Columbia ...
and Kenneth Steiglitz, ''Combinatorial Optimization: Algorithms and Complexity'', Corrected republication with a new preface, Dover.
*
Alexander Schrijver
Alexander (Lex) Schrijver (born 4 May 1948 in Amsterdam) is a Dutch mathematician and computer scientist, a professor of discrete mathematics and optimization at the University of Amsterdam and a fellow at the Centrum Wiskunde & Informatica in ...
, ''Theory of Linear and Integer Programming''. John Wiley & sons, 1998,
External links
EE364b a Stanford course homepage
{{optimization algorithms, convex
Combinatorial optimization
Convex optimization
Linear programming