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**Owning Class: **optimization

**Requires: **MathScript RT Module

xmin = fmin_qp(q, c, aineq, bineq)

xmin = fmin_qp(q, c, aineq, bineq, aeq, beq)

xmin = fmin_qp(q, c, aineq, bineq, aeq, beq, min, max)

[xmin, fval] = fmin_qp(q, c, aineq, bineq)

[xmin, fval] = fmin_qp(q, c, aineq, bineq, aeq, beq)

[xmin, fval] = fmin_qp(q, c, aineq, bineq, aeq, beq, min, max)

[xmin, fval, lambda] = fmin_qp(q, c, aineq, bineq)

[xmin, fval, lambda] = fmin_qp(q, c, aineq, bineq, aeq, beq)

[xmin, fval, lambda] = fmin_qp(q, c, aineq, bineq, aeq, beq, min, max)

Legacy Name: `quadprog`

Computes the minimum of a quadratic function defined by the following expression: 0.5**x*'***q****x*+**c**'**x*. LabVIEW constrains the computation based on the inputs that you specify.

Name | Description |
---|---|

q |
Specifies a matrix that is part of the quadratic function whose minimum you want to compute. q is a real, double-precision matrix. |

c |
Specifies a vector that is part of the quadratic function whose minimum you want to compute. c is a real, double-precision vector. |

aineq |
Specifies a matrix for the linear inequality constraints according to the following equation: aineq*xmin <= bineq. aineq can be []. aineq is a real, double-precision matrix. |

bineq |
Specifies a vector for the linear inequality constraints according to the following equation: aineq*xmin <= bineq. bineq must be [] when aineq is []. bineq is a real, double-precision vector. |

aeq |
Specifies a matrix for the linear equality constraints according to the following equation: aeq*xmin = beq. aeq can be []. aeq is a real, double-precision matrix. |

beq |
Specifies a vector for the linear equality constraints according to the following equation: aeq*xmin = beq. beq must be [] when aeq is []. beq is a real, double-precision vector. |

min |
Specifies the lower bound for the solution vector according to the following equation: min <= x <= max. min can be []. min is a real, double-precision vector. |

max |
Specifies the upper bound for the solution vector according to the following equation: min <= x <= max. max can be []. max is a real, double-precision vector. |

Name | Description |
---|---|

xmin |
Returns the point at which the quadratic function has the minimum value. xmin is a real, double-precision vector. |

fval |
Returns the value of the quadratic function evaluated at xmin. fval is a real, double-precision scalar. |

lambda |
Returns the lambda values for the solution vector. lambda is a real, double-precision vector. |

The following table lists the support characteristics of this function.

Supported in the LabVIEW Run-Time Engine | Yes |

Supported on RT targets | Yes |

Suitable for bounded execution times on RT | Not characterized |

Q = [1, 1; 1, 2];

C = [-2; -6];

AINEQ = [0.5, 0.5; -1, 2];

BINEQ = [1, 2];

MIN = [0; 0];

XMIN = fmin_qp(Q, C, AINEQ, BINEQ, [], [], MIN);

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