This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Problem-Based Optimization Algorithms

Internally, the solve function solves optimization problems by calling a solver:

  • linprog for linear objective and linear constraints

  • intlinprog for linear objective and linear constraints and integer constraints

  • quadprog for quadratic objective and linear constraints

  • lsqlin or lsqnonneg for linear least-squares with linear constraints

Before solve can call these functions, the problems must be converted to solver form, either by solve or some other associated functions or objects. This conversion entails, for example, linear constraints having a matrix representation rather than an optimization variable expression.

The first step in the algorithm occurs as you place optimization expressions into the problem. An OptimizationProblem object has an internal list of the variables used in its expressions. Each variable has a linear index in the expression, and a size. Therefore, the problem variables have an implied matrix form. The prob2struct function performs the conversion from problem form to solver form. For an example, see Convert Problem to Structure.

For the default and allowed solvers that solve calls, depending on the problem objective and constraints, see 'solver'. You can override the default by using the 'solver' name-value pair argument when calling solve.

For the algorithm that intlinprog uses to solve MILP problems, see intlinprog Algorithm. For the algorithms that linprog uses to solve linear programming problems, see Linear Programming Algorithms. For the algorithms that quadprog uses to solve quadratic programming problems, see Quadratic Programming Algorithms. For the algorithms that lsqlin uses to solve linear least-squares problems, see Least-Squares (Model Fitting) Algorithms.


If your objective function is a sum of squares, and you want solve to recognize it as such, write it as sum(expr.^2), and not as expr'*expr. The internal parser recognizes only explicit sums of squares. For an example, see Nonnegative Least-Squares, Problem-Based.

See Also

| |

Related Topics