Further reading

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We are constrained by time in this course, and there is a lot more to learn about in terms of optimisation methods. This page lists many references to important keywords in this field.

Gradient descent methods

• Learn how to solve quadratic problems with SLSQP;
• Learn about quasi-Newton methods for fast gradient converging methods like BFGS;
• Learn about constrained optimisation with the Karush-Kuhn-Tucker (KKT) theorem.

Optimisation for Machine Learning

• Learn about common optimisation methods for ML

Linear programming

• Learn about interior-point methods;
• Learn about the duality theorem

Mixed integer linear programming

• Learn about common ways to linearise constraints;
• Learn about more advanced cuts, e.g. Gomory cuts;
• Learn about advanced modelling tricks, like Benders’ decomposition or Dantzig-Wolfe decomposition

A quick introduction to complexity

• Learn about polynomial reductions;
• Learn about complexity categories above NP

Constraint programming

• Learn about more propagation mechanisms for global constraints;
• Learn about local search and it fits CP for large-neighbourhood search;
• Learn about SAT and SMT theories

Evolutionary methods

• Learn about more metaheuristics such as Particle Swarm Optimisation, Ant Colony Optimisation, and more;
• Learn about genetic programming, and see how we can make programs, graphs and networks evolve;
• Learn how evolutionary methods can help optimise the training of neural networks hyperparameters