# cobyqa.subsolvers.constrained_tangential_byrd_omojokun#

cobyqa.subsolvers.constrained_tangential_byrd_omojokun(grad, hess_prod, xl, xu, aub, bub, aeq, delta, debug, **kwargs)[source]#

Minimize approximately a quadratic function subject to bound and linear constraints in a trust region.

This function solves approximately

$\begin{split}\min_{s \in \mathbb{R}^n} \quad g^{\mathsf{T}} s + \frac{1}{2} s^{\mathsf{T}} H s \quad \text{s.t.} \quad \left\{ \begin{array}{l} l \le s \le u,\\ A_{\scriptscriptstyle I} s \le b_{\scriptscriptstyle I},\\ A_{\scriptscriptstyle E} s = 0,\\ \lVert s \rVert \le \Delta, \end{array} \right.\end{split}$

using an active-set variation of the truncated conjugate gradient method.

Parameters:
gradnumpy.ndarray, shape (n,)

Gradient $$g$$ as shown above.

hess_prodcallable

Product of the Hessian matrix $$H$$ with any vector.

hess_prod(s) -> numpy.ndarray, shape (n,)

returns the product $$H s$$.

xlnumpy.ndarray, shape (n,)

Lower bounds $$l$$ as shown above.

xunumpy.ndarray, shape (n,)

Upper bounds $$u$$ as shown above.

aubnumpy.ndarray, shape (m_linear_ub, n)

Coefficient matrix $$A_{\scriptscriptstyle I}$$ as shown above.

bubnumpy.ndarray, shape (m_linear_ub,)

Right-hand side $$b_{\scriptscriptstyle I}$$ as shown above.

aeqnumpy.ndarray, shape (m_linear_eq, n)

Coefficient matrix $$A_{\scriptscriptstyle E}$$ 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 $$s$$.

Other Parameters:
improve_tcgbool, optional

If True, a solution generated by the truncated conjugate gradient method that is on the boundary of the trust region is improved by moving around the trust-region boundary on the two-dimensional space spanned by the solution and the gradient of the quadratic function at the solution (default is True).

Notes

This function implements Algorithm 6.3 of [1]. It is assumed that the origin is feasible with respect to the bound and linear 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.