# cobyqa.subsolvers.cauchy_geometry#

cobyqa.subsolvers.cauchy_geometry(const, grad, curv, xl, xu, delta, debug)[source]#

Maximize approximately the absolute value of a quadratic function subject to bound constraints in a trust region.

This function solves approximately

$\begin{split}\max_{s \in \mathbb{R}^n} \quad \bigg\lvert c + g^{\mathsf{T}} s + \frac{1}{2} s^{\mathsf{T}} H s \bigg\rvert \quad \text{s.t.} \quad \left\{ \begin{array}{l} l \le s \le u,\\ \lVert s \rVert \le \Delta, \end{array} \right.\end{split}$

by maximizing the objective function along the constrained Cauchy direction.

Parameters:
constfloat

Constant $$c$$ as shown above.

gradnumpy.ndarray, shape (n,)

Gradient $$g$$ as shown above.

curvcallable

Curvature of $$H$$ along any vector.

curv(s) -> float

returns $$s^{\mathsf{T}} H s$$.

xlnumpy.ndarray, shape (n,)

Lower bounds $$l$$ as shown above.

xunumpy.ndarray, shape (n,)

Upper bounds $$u$$ as shown above.

deltafloat

Trust-region radius $$\Delta$$ as shown above.

debugbool

Whether to make debugging tests during the execution.

Returns:
numpy.ndarray, shape (n,)

Approximate solution $$s$$.

Notes

This function is described as the first alternative in Section 6.5 of [1]. It is assumed that the origin is feasible with respect to the bound constraints and that delta is finite and positive.

References

[1]

T. M. Ragonneau. Model-Based Derivative-Free Optimization Methods and Software. PhD thesis, Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China, 2022. URL: https://theses.lib.polyu.edu.hk/handle/200/12294.