cobyqa.subsolvers.spider_geometry#
- cobyqa.subsolvers.spider_geometry(const, grad, curv, xpt, 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 given straight lines.
- Parameters:
- constfloat
Constant \(c\) as shown above.
- grad
numpy.ndarray, shape (n,) Gradient \(g\) as shown above.
- curvcallable
Curvature of \(H\) along any vector.
curv(s) -> floatreturns \(s^{\mathsf{T}} H s\).
- xpt
numpy.ndarray, shape (n, npt) Points defining the straight lines. The straight lines considered are the ones passing through the origin and the points in xpt.
- xl
numpy.ndarray, shape (n,) Lower bounds \(l\) as shown above.
- xu
numpy.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 second 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.