The Linear Programming Solver
- Overview
- Getting Started
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Syntax
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Details
Presolve Pricing Strategies for the Primal and Dual Simplex Solvers The Network Simplex Algorithm The Interior Point Algorithm Macro Variable OROPTMODEL Iteration Log for the Primal and Dual Simplex Solvers Iteration Log for the Network Simplex Solver Iteration Log for the Interior Point Solver Problem Statistics Data Magnitude and Variable Bounds Variable and Constraint Status Irreducible Infeasible Set
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Examples
Diet Problem Reoptimizing the Diet Problem Using BASIS=WARMSTART Two-Person Zero-Sum Game Finding an Irreducible Infeasible Set Using the Network Simplex Solver Migration to OPTMODEL: Generalized Networks Migration to OPTMODEL: Maximum Flow Migration to OPTMODEL: Production, Inventory, Distribution Migration to OPTMODEL: Shortest Path
- References
| Data Magnitude and Variable Bounds |
Extremely large numerical values might cause computational difficulties for the LP solver, but the occurrence of such difficulties is hard to predict. For this reason, the LP solver issues a data error message whenever it detects model data that exceeds a specific threshold number. The value of the threshold number depends on your operating environment and is printed in the log as part of the data error message.
The following conditions produce a data error:
The absolute value of an objective coefficient, constraint coefficient, or range (difference between the upper and lower bounds on a constraint) is greater than the threshold number.
A variable’s lower bound, a
or 
constraint’s right-hand side, or a range constraint’s lower bound is greater than the threshold number. A variable’s upper bound, a
or constraint’s right-hand side, or a range constraint’s upper bound is smaller than the negative threshold number.
If a variable’s upper bound is larger than 1E20, then the LP solver treats the bound as 
. Similarly, if a variable’s lower bound is smaller than –1E20, then the LP solver treats the bound as 
.