A Globally Convergent Third-Order Newton Method via Unified Semidefinite Programming Subproblems

Abstract

We propose the Adaptive Levenberg-Marquardt Third-Order Newton Method (ALM-TON) method for unconstrained nonconvex optimization; to our knowledge, the framework provides the first globally convergent realization of the unregularized third-order Newton method. Unlike the standard Adaptive Regularization framework with third-order models (AR3), which enforces global behavior through a quartic term, ALMTON employs an adaptive Levenberg-Marquardt (quadratic) regularization. This choice preserves a cubic model at every iteration, so that every subproblem is a tractable semidefinite programming (SDP). Algorithmically, ALMTON follows a mixed-mode strategy: it attempts an unregularized thirdorder step whenever the cubic Taylor model admits a strict local minimizer with adequate curvature, and activates (or increases) quadratic regularization only when needed to ensure that the model is well posed and the step is globally reliable. For the Heuristic strategy, under the stated assumptions and an exact local-minimizer oracle, we prove finite termination at an $\epsilon$-approximate first-order stationary point with $O\left({\epsilon }^{-2}\right)$ worst-case evaluation complexity. Moreover, if an accepted iterate enters the stated neighborhood of a positive-definite local minimizer, subsequent nonterminal steps recover the unregularized third-order Newton recursion and its cubic local rate. Under a common post hoc terminal audit over 4,500 deterministic starts on five two-dimensional nonconvex problems, both ALMTON variants satisfy the terminal criterion on $99.91\%$ of the instances, compared with $55.42\%$ for the unregularized third-order Newton method. This robustness gain comes at substantial SDP cost: AR$2$ is faster, so the results support robust globalization of the unregularized cubic model rather than overall empirical superiority.

33 pages, 5 figures