StructOpt is introduced as a structural layer that enhances the stability of existing optimizers such as SGD and Adam, rather than replacing them. It modulates the effective step scale based on an internal structural signal, S(t), which responds to instability in the optimization process. This approach aims to stabilize the optimization trajectory in challenging landscapes where traditional methods may diverge or exhibit large oscillations. The effectiveness of StructOpt is demonstrated through two stress tests. The first involves a controlled oscillatory landscape where vanilla SGD diverges and Adam shows significant step oscillations. StructOpt successfully stabilizes the trajectory by dynamically adjusting the step size without requiring explicit tuning. The second test involves a regime shift where the loss landscape changes abruptly. Here, the structural signal S(t) acts like a damping term, reacting to instability spikes and maintaining bounded optimization. StructOpt is presented as a stability layer that can be composed on top of existing optimization methods, rather than competing with them. The signal S(t) is shown to correlate with instability rather than gradient magnitude, suggesting its potential as a general mechanism for improving stability. The approach is optimizer-agnostic and invites feedback on its applicability and potential failure modes. The code is designed for inspection rather than performance, encouraging further exploration and validation. This matters because enhancing the stability of optimization processes can lead to more reliable and robust outcomes in machine learning and other computational fields.
