Before you begin to solve an optimization problem, you must choose the appropriate approach: problem-based or solver-based. For details, see First Choose Problem-Based or Solver-Based Approach.
For 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 Optimization Workflow. To
solve the resulting problem, use
For the solver-based steps to take, including defining the objective
function and constraints, and choosing the appropriate solver, see Solver-Based Optimization Problem Setup. To solve the
resulting problem, use
Shows how to solve a problem-based quadratic programming problem with bound constraints using different algorithms.
Shows how to solve a large sparse quadratic program using the problem-based approach.
Example showing large-scale problem-based quadratic programming.
Example showing problem-based quadratic programming on a basic portfolio model.
Example of quadratic programming with bound constraints.
This example shows the benefit of the active-set algorithm on problems with many linear constraints.
Example showing how to save memory in a structured quadratic program.
Example showing how to save memory in a quadratic program by using a sparse quadratic matrix.
Example showing solver-based large-scale quadratic programming.
Example showing solver-based quadratic programming on a basic portfolio model.
Generate C code for quadratic optimization.
Learn the basics of code generation for the
Explore techniques for handling real-time requirements in generated code.
How the optimization functions and objects solve optimization problems.
Lists all available mathematical and indexing operations on optimization variables and expressions.