This glossary explains the key concepts and terms for Optimizer.
| Term | Definition |
| Constraint | A restriction expressed as an inequality, such as limiting a value to be less than, greater than, or not negative. |
| Feasibility | An alternative to finding the optimal solution is to find the first feasible solution. |
| Input data | Values necessary for computing the solution, including any constraints. |
| Integer linear program | A linear program where variables are constrained to integral values (whole numbers). |
| Linear function | A function with a polynomial of degree 0 or 1. Displays as a straight line on a graph. |
| Linear program | A program where all variables are continuous or real, and the objective function and constraints are linear. |
| Mixed Integer Linear Program (MILP) | A linear program where some of the variables have integral values (whole numbers), and some have continuous or real values (decimal numbers). |
| MIP Gap | For mixed integer linear problems, the Optimizer compares the current best solution with the theoretically best possible solution. The optimization is successful when the gap between those values is within a specific tolerance (the MIP Gap). |
| Objective | An expression that guides the optimization engine while it determines which assignments best support the business goal or solution, such as maximum income or minimal expense. |
| Optimality | An optimal solution to a problem with:
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| Time-out | The number of seconds until an Optimizer action that is processing stops and abandons progress. This value must be set when creating an Optimizer action to prevent Optimizer from running indefinitely if a problem is unsolvable. |
| Upper bound, Lower bound | The maximum or minimum value for a variable. |
| Variable | The value that represents the solution to the problem (sometimes called the decision variable). |
Variable data type (Variables must have a numeric data type) | Integer (whole number) Real (decimal, or floating point) Binary (zero or one, can be called Boolean) |
