DUAL-ENGINE DRIVE OF DATA + MODEL ON OPTIMIZATION THEORY FOR COURSE FOR THE AI ERA
Keywords:
Model-driven optimization, Integrated perception, Data-driven optimization, Bidirectional empowerment of AI and optimization, Prediction-decision integrationAbstract
Addressing the shift in AI-era optimization algorithms from offline static to integrated perception, this paper proposes a three-tiered pyramid teaching framework. By introducing computational graph perspectives to reshape foundational theory, empowering advanced training through dual-layer programming, and driving end-to-end differentiable optimization applications drives application practice, effectively resolving the challenges of fragmented knowledge, outdated tools, and single-dimensional assessment in traditional curricula. Practice demonstrates that this framework significantly enhances graduate students' dual-habitat cross-disciplinary problem-solving capabilities, bridging mathematical rigor and AI intuition.References
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