arXiv:2601.16510v3 Announce Type: replace-cross Abstract: Solving massive-scale optimization problems requires scalable first-order methods with low per-iteration cost. This tutorial highlights a shift in optimization: using differentiable programming not only to execute algorithms but to learn how to design them. Modern frameworks such as PyTorch, TensorFlow, and JAX enable this paradigm through efficient automatic differentiation. Embedding first-order methods within these systems allows end-to-end training that improves convergence and solution quality. Guided by Fenchel-Rockafellar duality
The proliferation of advanced AI models and the increasing complexity of optimization problems in various domains necessitate more efficient and scalable solutions, making differentiable programming a timely approach.
This development can significantly accelerate the design and improvement of AI algorithms, leading to more powerful and efficient AI systems across all sectors, from scientific research to industrial applications.
Traditional manual algorithm design is being augmented and potentially superseded by AI-driven, end-to-end learning processes, fundamentally altering how optimization methods are developed and evolved.
- · AI algorithm developers
- · Cloud computing providers
- · Industries relying on optimization (logistics, finance, manufacturing)
- · AI hardware manufacturers
- · Software engineers specializing in manual algorithm tuning
- · Legacy optimization software vendors
Optimization problems in AI training and deployment become significantly more efficient, reducing computational costs and time.
This efficiency enables the development of AI models that are orders of magnitude more complex or capable than current designs.
The ability of AIs to 'learn how to learn' in optimization could lead to unforeseen breakthroughs in scientific discovery and engineering, accelerating the pace of innovation across many fields.
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Read at arXiv cs.LG