Method get_starting_point

with prototype

virtual bool get_starting_point(Index n, bool init_x, Number* x,
                                bool init_z, Number* z_L, Number* z_U,
                                Index m, bool init_lambda, Number* lambda)

Give IPOPT the starting point before it begins iterating.

• n: (in), the number of variables in the problem (dimension of $x$).
• init_x: (in), if true, this method must provide an initial value for $x$.
• x: (out), the initial values for the primal variables, $x$.
• init_z: (in), if true, this method must provide an initial value for the bound multipliers $z^L$ and $z^U$.
• z_L: (out), the initial values for the bound multipliers, $z^L$.
• z_U: (out), the initial values for the bound multipliers, $z^U$.
• m: (in), the number of constraints in the problem (dimension of $g(x)$).
• init_lambda: (in), if true, this method must provide an initial value for the constraint multipliers, $\lambda$.
• lambda: (out), the initial values for the constraint multipliers, $\lambda$.

The variables n and m are passed in for your convenience. These variables will have the same values you specified in get_nlp_info.

Depending on the options that have been set, IPOPT may or may not require bounds for the primal variables $x$, the bound multipliers $z^L$ and $z^U$, and the constraint multipliers $\lambda$. The boolean flags init_x, init_z, and init_lambda tell you whether or not you should provide initial values for $x$, $z^L$, $z^U$, or $\lambda$ respectively. The default options only require an initial value for the primal variables $x$. Note, the initial values for bound multiplier components for ``infinity'' bounds ( $x_L^{(i)}=-\infty$ or $x_U^{(i)}=\infty$) are ignored.

In our example, we provide initial values for $x$ as specified in the example problem. We do not provide any initial values for the dual variables, but use an assert to immediately let us know if we are ever asked for them.

bool HS071_NLP::get_starting_point(Index n, bool init_x, Number* x,
                                   bool init_z, Number* z_L, Number* z_U,
                                   Index m, bool init_lambda,
                                   Number* lambda)
{
  // Here, we assume we only have starting values for x, if you code
  // your own NLP, you can provide starting values for the dual variables
  // if you wish to use a warmstart option
  assert(init_x == true);
  assert(init_z == false);
  assert(init_lambda == false);

  // initialize to the given starting point
  x[0] = 1.0;
  x[1] = 5.0;
  x[2] = 5.0;
  x[3] = 1.0;

  return true;
}