Optimisation

• Adam A Method for Stochastic Optimization
• Adam-mini Use Fewer Learning Rates To Gain More
• Estimation of Non-Normalized Statistical Models by Score Matching
• Full Parameter Fine-tuning for Large Language Models with Limited Resources - “LOMO”
• How to Train Your Energy-Based Models
• Large Batch Optimization for Deep Learning Training BERT in 76 minutes
• Large Batch Training of Convolutional Networks
• LoRA Low-Rank Adaptation of Large Language Models
• Noise-contrastive estimation A new estimation principle for unnormalized statistical models
• Parameter-efficient fine-tuning of large-scale pre-trained language models - “Delta-tuning”
• SAGA A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
• SGDR Stochastic Gradient Descent with Warm Restarts
• Stochastic Average Gradient A Simple Empirical Investigation
• The Marginal Value of Adaptive Gradient Methods in Machine Learning

Resources 📚

• Convex Optimization by Ryan Tibshirani
• Convex Optimization by Stephen Boyd and Lieven Vandenberghe
• Foundations for Optimization and Optimization by Mark Walker
• Short Lectures on Optimization by Michel Bierlaire
• bentrevett/a-tour-of-pytorch-optimizers - A tour of different optimization algorithms in PyTorch
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• 👉 Optimisation - Loss Functions

Concepts

• Karush–Kuhn–Tucker conditions - Wikipedia

Optimization of Logistic and Multinomial Regression

• Chapter 5 on Logistic Regression from Speech and Language Processing. Daniel Jurafsky & James H. Martin contains explanations and derivations of the optimisation of logistic and multinomial regression. Relevant sections are:
  • §5.4 Learning in Logistic Regression
  • §5.5 The cross-entropy loss function
  • §5.6.1 The Gradient for Logistic Regression (and the worked example in 5.6.3)
  • §5.8 Learning in Multinomial Logistic Regression
  • §5.10 Advanced: Deriving the Gradient Equation
• A comparison of numerical optimizers for logistic regression from Thomas P. Minka (October 22, 2003; revised Mar 26, 2007)
• This post (responding the question of how scikit-learn optimisers work) briefly explains:
  • Newton’s method
  • Limited-memory Broyden–Fletcher–Goldfarb–Shanno Algorithm (L-BFGS)
  • LIBLINEAR, the Library for Large Linear Classification (from ICML 2008)
  • SAG
  • SAGA A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
• Broyden–Fletcher–Goldfarb–Shanno algorithm - Wikipedia
• How does the L-BFGS work? (Cross Validated post)

Lagrangian Optimisation

• Lagrange Multipliers | Geometric Meaning & Full Example from Dr. Trefor Bazett - excellent visual intuition motivating the Lagrange multiplier method via contours (level curves) and gradients (the gradient of the constraint curve at the max/min is a scalar multiple of the gradient of the main, loss or in economics “utility” function)
• Lagrangian Optimization: Example from EconomicsHelpDesk (June 9th 2013)
• Lagrange multipliers, using tangency to solve constrained optimization from Khan Academy

Optimising Support Vector Machines

• CS 229, Autumn 2009 The Simplified SMO Algorithm

See also

• Duality (optimization) - Wikipedia