Differentiable Programming

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Learning to Optimize by Differentiable Programming
Jan 23, 2026Liping Tao, Xindi Tong, Chee Wei TanPrimal-Dual Optimization Learning to Optimize
Nanyang Technological University, Singapore · Nanyang Technological University, Singapore, Singapore
PyDPF: A Python Package for Differentiable Particle Filtering
Oct 29, 2025John-Joseph Brady, Benjamin Cox, Yunpeng Li +1Particle Filtering Bayesian Filtering
King’s College London · Max-Planck Institut für Physik · University of Edinburgh
Unraveling the iterative CHAD
May 21, 2025Fernando Lucatelli Nunes, Gordon Plotkin, Matthijs VákárDifferentiable Programming
Department of Information and Computing Sciences, Utrecht University, Netherlands · Google DeepMind, Google DeepMind, USA

Jan 23, 2026, cs.MSJ/K move · Enter open · S save

Learning to Optimize by Differentiable Programming

Liping Tao, Xindi Tong, Chee Wei Tan

Nanyang Technological University, Singapore · Nanyang Technological University, Singapore, Singapore

Primal-Dual Optimization Learning to Optimize Alternating Direction Method of Multipliers Differentiable Programming Constrained Optimization Differentiable Optimization

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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 tutorial demonstrates how duality-informed iterative schemes such as the alternating direction method of multipliers, and the primal-dual hybrid gradient can be learned and adapted through representative case studies.

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Latest papers 33

Learning to Optimize by Differentiable Programming

PyDPF: A Python Package for Differentiable Particle Filtering

Unraveling the iterative CHAD