Presolver
From Wikimization
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\end{array} | \end{array} | ||
</math></center> | </math></center> | ||
- | suggest that a polyhedral cone | + | suggest that a ''polyhedral cone'' comes into play. |
- | + | Geometers regard cones as convex Euclidean bodies that are semi-infinite in extent. | |
- | + | Examples of finite polyhedral cones are the great Pyramids of Egypt, | |
- | while finite circular cones hold ice cream and block traffic. | + | while finite circular cones hold ice cream and block traffic in daily life. |
- | + | A geometer defines a polyhedral (semi-infinite) cone <math>\,\mathcal{K}</math> in <math>\,\reals^m</math> thus: | |
<center><math>\mathcal{K}=\{A_{}x~|~x\succeq0\}</math></center> | <center><math>\mathcal{K}=\{A_{}x~|~x\succeq0\}</math></center> | ||
+ | To visualize a polyhedral cone in three dimensions, think of one Egyptian Pyramid continuing into the ground | ||
+ | and then out into space from the opposite side of Earth. | ||
+ | Four edges correspond to four columns from matrix <math>\,A</math>. | ||
+ | But <math>\,A</math> can have more than four columns and still describe a Pyramid. | ||
+ | When it does, then four columns define that Pyramid's edges while each remaining column resides interior to the cone or on one of its ''faces'' (a flat side in 3D, defined more formally later). |
Revision as of 22:50, 8 August 2011
Introduction
Presolving conventionally means quick elimination of some variables and constraints prior to numerical solution of an optimization problem. Presented with constraints for example, a presolver is likely to check whether constant vector is positive; for if so, variable can have only the trivial solution. The effect of such tests is to reduce the problem dimensions.
Most commercial optimization problem solvers incorporate presolving. Particular reductions can be proprietary or invisible, while some control or selection may be given to a user. But all presolvers have the same motivation: to make an optimization problem smaller and (ideally) easier to solve. There is profit potential because a solver can then compete more effectively in the marketplace for large-scale problems.
We present a method for reducing variable dimension based upon geometry of constraints in the problem statement:
where is a matrix of predetermined dimension, represents the integers, the real numbers, and is some possibly empty index set.
The caveat to use of our proposed method for presolving is that it is not fast. One would incorporate this method only when a problem is too big to be solved; that is, when solver software chronically exits with error or hangs.
Geometry of Constraints
The idea, central to our method for presolving, is more easily understood geometrically. Constraints
suggest that a polyhedral cone comes into play. Geometers regard cones as convex Euclidean bodies that are semi-infinite in extent. Examples of finite polyhedral cones are the great Pyramids of Egypt, while finite circular cones hold ice cream and block traffic in daily life. A geometer defines a polyhedral (semi-infinite) cone in thus:
To visualize a polyhedral cone in three dimensions, think of one Egyptian Pyramid continuing into the ground and then out into space from the opposite side of Earth. Four edges correspond to four columns from matrix . But can have more than four columns and still describe a Pyramid. When it does, then four columns define that Pyramid's edges while each remaining column resides interior to the cone or on one of its faces (a flat side in 3D, defined more formally later).