TOMVIEW Quickguide. 8 QPCON Problem

8 QPCON Problem

When solving a problem with a quadratic objective and nonlinear constraints TOMVIEW automatically supplies objective derivatives (gradient and Hessian) if qpconAssign is used.

The quadratic constrained nonlinear programming problem is defined as:

min

f(x) =

x^T F x + d^T x

s/t

x_L	≤	x	≤	x_U
b_L	≤	A x	≤	b_U
c_L	≤	c(x)	≤	c_U

(10)

where x, x_L, x_U, d

Rⁿ, F

R^{n
× n}, f(x)

R, A

R^{m₁ ×
n}, b_L,b_U

R^m₁ and c_L,c(x),c_U

R^m₂.

The following files define and solve an example problem in TOMLAB.

File: quickguide/qpconQG.vi, qpconQG_c.vi, qpconQG_dc.vi, qpcon_d2c.vi

  c:   Nonlinear constraint vector
  dc:  Nonlinear constraint gradient matrix
  d2c: The second part of the Hessian to the Lagrangian function for the nonlinear constraints.

The following file illustrates how to solve this QPCON (QP-NLP) problem in TOMVIEW. Also view the other VI's specified above for more information.

It is possible to change the output displayed by expanding the cluster in the block diagram.

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