• Duchi - Introductory Lectures on Stochastic Optimization
• Bottou et al 2016 - Optimization Methods for Large-Scale Machine Learning Survey paper that includes theory results with proofs (even includes the rate of convergence of SGD on non-convex objectives)
• Black-box Optimization
• Boyd & Vandenberghe - Convex Optimization
• Luenberger - Linear and Nonlinear Programming Great book

• Agarwal, Leon Bottou 2015 - A Lower Bound for the Optimization of Finite Sums
• Ghadimi & Lan 2013 - Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming Cited by Jin 2021 for Theorem 2.10
• Jin et al 2021 - On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points Overview of results

• 2018 - Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates
• 2015 - Cyclical Learning Rates for Training Neural Networks
• 2020 - On the Convergence of Nesterov's Accelerated Gradient Method in Stochastic Settings
• Mei et al 2023 - Stochastic Gradient Succeeds for Bandits

See also Courses and Seminars.

• Large-Scale Optimization for Data Science @ Princeton
• Mathematical Data Science Reading Group @ Princeton
• EPFL Course - Optimization for Machine Learning
• Convex Optimization and Approximation @ Berkeley

• 2018 - Setting the learning rate of your neural network
• 2017 - The Two Phases of Gradient Descent in Deep Learning
• Optimization For Training Deep Models Part I

• Dale Schuurmans

• Application: Optimization
• NN Training
• Optimizers
• Optimization in Deep Learning