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 solve.
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 quadprog.
Quadratic Programming with Bound Constraints: Problem-Based
Shows how to solve a problem-based quadratic programming problem with bound constraints using different algorithms.
Large Sparse Quadratic Program, Problem-Based
Shows how to solve a large sparse quadratic program using the problem-based approach.
Bound-Constrained Quadratic Programming, Problem-Based
Example showing large-scale problem-based quadratic programming.
Quadratic Programming for Portfolio Optimization, Problem-Based
Example showing problem-based quadratic programming on a basic portfolio model.
Quadratic Minimization with Bound Constraints
Example of quadratic programming with bound constraints.
Quadratic Programming with Many Linear Constraints
This example shows the benefit of the active-set algorithm on problems with many linear constraints.
Quadratic Minimization with Dense, Structured Hessian
Example showing how to save memory in a structured quadratic program.
Large Sparse Quadratic Program with Interior Point Algorithm
Example showing how to save memory in a quadratic program by using a sparse quadratic matrix.
Bound-Constrained Quadratic Programming, Solver-Based
Example showing solver-based large-scale quadratic programming.
Quadratic Programming for Portfolio Optimization Problems, Solver-Based
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 quadprog
optimization solver.
Optimization Code Generation for Real-Time Applications
Explore techniques for handling real-time requirements in generated code.
Problem-Based Optimization Algorithms
How the optimization functions and objects solve optimization problems.
Supported Operations on Optimization Variables and Expressions
Lists all available mathematical and indexing operations on optimization variables and expressions.
Quadratic Programming Algorithms
Minimizing a quadratic objective function in n dimensions with only linear and bound constraints.
Optimization Options Reference
Explore optimization options.