Effective medical image registration aims to align anatomical structures accurately and apply smooth, plausible transformations across diverse imaging tasks. Deep learningbased registration approaches require extensive training data and task-specific configurations, which limits their adaptability and usability across multiple modalities and anatomical regions. We present ConvexAdam [1], a dual-optimization framework that combines convex optimization for global alignment with Adam-based instance optimization for fine-tuning.