Deep neural networks often encounter non-convex optimization challenges during training due to the presence of local minima, saddle points, and complex loss surfaces. Existing optimization algorithms such as Adam and Stochastic Gradient Descent (SGD) offer complementary advantages—Adam provides faster convergence, while SGD tends to achieve better generalization. However, neither optimizer alone effectively balances both properties in non-convex settings. To address this limitation, this paper proposes a phase-switch hybrid optimization strategy that combines the strengths of Adam and SGD. The proposed method employs Adam during the initial phase of training to enable rapid convergence and efficient exploration of the loss landscape, and then switches to momentum-based SGD in the later phase to improve generalization and ensure stable convergence. The effectiveness of the proposed approach is evaluated on three benchmark dataset the M4 time-series forecasting data set, under different learning rate settings. Experimental results demonstrate that the proposed method achieves performance that is superior or comparable to existing optimizers in terms of accuracy and loss minimization. These results indicate that the proposed hybrid optimization strategy provides a simple and effective solution for handling non-convex optimization problems
Deep neural networks often encounter non-convex optimization challenges during training due to the presence of local minima, saddle points, and complex loss surfaces. Existing optimization algorithms such as Adam and Stochastic Gradient Descent (SGD) offer complementary advantages—Adam provides faster convergence, while SGD tends to achieve better generalization. However, neither optimizer alone effectively balances both properties in non-convex settings. To address this limitation, this paper proposes a phase-switch hybrid optimization strategy that combines the strengths of Adam and SGD. The proposed method employs Adam during the initial phase of training to enable rapid convergence and efficient exploration of the loss landscape, and then switches to momentum-based SGD in the later phase to improve generalization and ensure stable convergence. The effectiveness of the proposed approach is evaluated on one benchmark dataset, MNIST dataset, under different learning rate settings. Experimental results demonstrate that the proposed method achieves performance that is superior or comparable to existing optimizers in terms of accuracy and loss minimization. These results indicate that the proposed hybrid optimization strategy provides a simple and effective solution for handling non-convex optimization problems in deep learning.