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A Newton-bracketing method for a simple conic optimization problem
For the Lagrangian-DNN relaxation of quadratic optimization problems (QOPs),
we propose a Newton-bracketing method to improve the performance of the
bisection-projection method implemented in BBCPOP [to appear in ACM Tran.
Softw., 2019]. The relaxation problem is converted into the problem of finding
the largest zero of a continuously differentiable (except at )
convex function such that if
and otherwise. In theory, the method generates lower
and upper bounds of both converging to . Their convergence is
quadratic if the right derivative of at is positive. Accurate
computation of is necessary for the robustness of the method, but it is
difficult to achieve in practice. As an alternative, we present a
secant-bracketing method. We demonstrate that the method improves the quality
of the lower bounds obtained by BBCPOP and SDPNAL+ for binary QOP instances
from BIQMAC. Moreover, new lower bounds for the unknown optimal values of large
scale QAP instances from QAPLIB are reported.Comment: 19 pages, 2 figure
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