This glossary explains the key concepts and terms for Optimizer.
| Term | Definition |
| Constraint | A limit on a value, such as its maximum, minimum, or that the value can’t be negative. |
| Feasibility | An alternative to optimality, this offers a possible solution to a problem with:
|
| 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 | The pursuit of a solution in the form of a real number, where the Objective Function and the constraints are linear. |
| 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). |
| Mixed integer linear program | A linear program where only some of the variables are constrained to integral values. Other variables can be real values (decimal numbers). |
| 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:
|
| 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) |
