There are two approaches to linear programming, quadratic programming, and mixed-integer linear programming. This section describes the problem-based approach.
Create problem variables, and then represent the objective function
and constraints in terms of these symbolic variables. For the
problem-based steps to take, see Problem-Based Workflow. To
solve the resulting problem, use
For examples, see Linear Programming and Mixed-Integer Linear Programming or Quadratic Programming.
|Create optimization problem|
|Create optimization variables|
|Display variable bounds|
|Display optimization problem|
|Display optimization variable|
|Save description of variable bounds|
|Save optimization problem description|
|Save optimization variable description|
Problem-based steps for solving optimization problems.
Expressions define both objective and constraints.
How to create and work with named indices for variables.
Shows how to review or modify problem elements such as variables and constraints.
How to evaluate the solution and its quality.
Tips for obtaining a faster or more accurate solution when there are integer constraints, and for avoiding loops in problem creation.
To create reusable, scalable problems, separate the model from the data.
Solution to the problem of two optimization variables with the same name.
This example shows how to create initial points for
when you have named index variables by using the
Optimization expressions containing
NaN cannot be displayed, and can cause unexpected
How the optimization functions and objects solve optimization problems.
Lists all available mathematical and indexing operations on optimization variables and expressions.