Global Optimality Characterizations and Algorithms for Minimizing Quartically-Regularized Third-Order Taylor Polynomials

High-order methods for convex and nonconvex optimization, particularly $p$th-order Adaptive Regularization Methods (AR$p$), have attracted significant research interest by naturally incorporating high-order Taylor models into adaptive regularization frameworks, resulting in algorithms with faster global and local convergence rates than first- and second-order methods.

This paper establishes global optimality conditions for general, nonconvex cubic polynomials with quartic regularization.

These criteria generalise existing results, recovering the optimality results for regularized quadratic polynomials, and can be further simplified in the low-rank and diagonal tensor cases.

Under suitable assumptions on the Taylor polynomial, we derive a lower bound for the regularization parameter such that the necessary and sufficient criteria coincide, establishing a connection between this bound and the subproblem's convexification and sum-of-squares (SoS) convexification techniques.

Leveraging the optimality characterization, we develop a Diagonal Tensor Method (DTM) for minimizing quartically-regularized cubic Taylor polynomials by iteratively minimizing a sequence of local models that incorporate both diagonal cubic terms and quartic regularization (DTM model).

We show that the DTM algorithm is provably convergent, with a global evaluation complexity of $\mathcal{O}(\epsilon^{-3/2})$.

Furthermore, when special structure is present (such as low rank or diagonal), DTM can exactly solve the given problem (in one iteration).

In our numerical experiments, we propose practical DTM variants that exploit local problem information for model construction, which we then show to be competitive with cubic regularization and other subproblem solvers, with superior performance on problems with special structure.