# cobyqa.subsolvers.normal_byrd_omojokun#

cobyqa.subsolvers.normal_byrd_omojokun(aub, bub, aeq, beq, xl, xu, delta, debug, **kwargs)[source]#

Minimize approximately a linear constraint violation subject to bound constraints in a trust region.

This function solves approximately

$\begin{split}\min_{s \in \R^n} \quad \frac{1}{2} \big( \lVert \max \{ \aub s - \bub, 0 \} \rVert^2 + \lVert \aeq s - \beq \rVert^2 \big) \quad \text{s.t.} \quad \left\{ \begin{array}{l} \xl \le s \le \xu,\\ \lVert s \rVert \le \Delta, \end{array} \right.\end{split}$

using a variation of the truncated conjugate gradient method.

Parameters:
aubnumpy.ndarray, shape (m_linear_ub, n)

Matrix $$\aub$$ as shown above.

bubnumpy.ndarray, shape (m_linear_ub,)

Vector $$\bub$$ as shown above.

aeqnumpy.ndarray, shape (m_linear_eq, n)

Matrix $$\aeq$$ as shown above.

beqnumpy.ndarray, shape (m_linear_eq,)

Vector $$\beq$$ as shown above.

xlnumpy.ndarray, shape (n,)

Lower bounds $$\xl$$ as shown above.

xunumpy.ndarray, shape (n,)

Upper bounds $$\xu$$ 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:
improvebool, 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 6.4 of . It is assumed that the origin is feasible with respect to the bound constraints and that delta is finite and positive.

References



T. M. Ragonneau. Model-Based Derivative-Free Optimization Methods and Software. PhD thesis, The Hong Kong Polytechnic University, Hong Kong, China, 2022.