presets.md
In Uno: R Interface to the 'Uno' Nonlinear Optimization Solver

Uno presets

Uno implements presets, that is combinations of ingredients that correspond to existing solvers. At the moment, the available presets are filtersqp (after the trust-region restoration filter SQP solver filterSQP) and ipopt (after the line-search filter restoration infeasible interior-point solver IPOPT). We show below how the eight ingredients naturally arise in these two methods.

Convergence of SQP filter methods has been proven under mild conditions in the context of trust-region methods and of line-search methods. The trust-region optimality QP subproblem about $(x^{(k)}, y^{(k)})$ is defined as:

$$ \tag{$QP^{(k)}(\Delta^{(l)})$} \begin{array}{ll} \displaystyle \min_{d_x} & \frac{1}{2} d_x^T H_1^{(k)} d_x + (\nabla f^{(k)})^T d_x \ \mbox{s.t.} & c^{(k)} + (\nabla c^{(k)})^T d_x = 0 \ & x^{(k)} + d_x \ge 0 \ & \|d_x\|_\infty \le \Delta^{(l)}, \end{array} $$

where $\Delta^{(l)} > 0$ is the current trust-region radius. If the QP subproblem is infeasible ($\Delta^{(l)}$ is too small or the linearized constraints are inconsistent), we switch to feasibility restoration and solve a smooth reformulation of the $\ell_1$ feasibility problem with elastic variables $u^+ \in \mathbb{R}^m$ and $u^- \in \mathbb{R}^m$:

$$ \tag{$FQP^{(k)}(\Delta^{(l)})$} \begin{array}{ll} \displaystyle \min_{d_x, u^+, u^-} & \frac{1}{2} d_x^T H_0^{(k)} d_x + e^T u^+ + e^T u^- \ \mbox{s.t.} & c^{(k)} + (\nabla c^{(k)})^T d_x - u^+ + u^- = 0 \ & x^{(k)} + d_x \ge 0 \ & \|d_x\|_\infty \le \Delta^{(l)} \ & u^+ \ge 0, \; u^- \ge 0. \end{array} $$

If the trial iterate $x^{(k)} + d_x$ makes sufficient progress with respect to the filter method, it is accepted. If the trust region was active at the solution of the QP ($\|d_x^*\|_\infty = \Delta^{(l)}$), we enlarge the radius. If the trial iterate is rejected, we resolve the trust-region subproblem with a smaller trust-region radius.

An infeasible interior-point method does not require feasibility with respect to the general constraints. A prerequisite is to turn inequality constraints into equality constraints using slack variables:

$$ \begin{array}{ll} \displaystyle \min_{x,s} & f(x) \ \mbox{s.t.} & c(x) - s = 0 \ & l_x \le x \le u_x \ & l_c \le s \le u_c. \end{array} $$

Provided that the subproblem is convex, the primal-dual direction is the solution of the primal-dual system:

$$ \tag{$IPSP_\mu$} \begin{pmatrix} H_1^{(k)} + (X^{(k)})^{-1} Z^{(k)} + \delta_w I & \nabla c^{(k)} \ (\nabla c^{(k)})^T & -\delta_c I \end{pmatrix} \begin{pmatrix} d_x \ -d_y \end{pmatrix} = - \begin{pmatrix} \nabla f^{(k)} - \nabla c^{(k)} y^{(k)} - \mu (X^{(k)})^{-1} e \ c^{(k)} \end{pmatrix}, $$

where $X^{(k)} = \text{diag}(x^{(k)})$, $Z^{(k)} = \text{diag}(z^{(k)})$, $e$ is a vector of ones of appropriate size, and $\delta_w$ and $\delta_c$ are primal and dual inertia correction coefficients. The dual direction for the bound constraints is given by $d_z = (X^{(k)})^{-1} (\mu e - Z^{(k)} d_x) - z^{(k)}$. The fraction-to-boundary rule determines primal and dual step lengths that maintain positivity of $x$ and $z$:

$$ \begin{array}{ll} \alpha_x^{(k)} & \stackrel{\text{def}}{=} \max{\alpha \in (0, 1] \mid x^{(k)} + \alpha d_x \ge (1 - \tau) x^{(k)}} \ \alpha_z^{(k)} & \stackrel{\text{def}}{=} \max{\alpha \in (0, 1] \mid z^{(k)} + \alpha d_z \ge (1 - \tau) z^{(k)}}, \end{array} $$

where $\tau$ is a parameter close to 1. A filter line search assesses whether the trial iterate makes sufficient progress with respect to the filter method. If the step length ultimately falls below a given threshold (e.g., $10^{-7}$), we switch to feasibility restoration. Note that by construction the filter entries depend on the barrier parameter $\mu$ through the auxiliary measure $\xi$. Consequently, the filter must be flushed whenever $\mu$ is updated.