Module finder

The module provides an introduction to the theory and practice of optimization techniques. It covers linear programming as well as nonlinear programming. This module is suitable to those who want to apply computational optimization methods to their problems, which can arise from a variety of applied disciplines such as compuer science and engineering.

The module provides an introduction to the theory and practice of optimization techniques. It covers linear programming as well as nonlinear programming. This module is suitable to those who want to apply computational optimization methods to their problems, which can arise from a variety of applied disciplines such as compuer science and engineering.

Organisations are typically faced with many decision problems in the running of their operations and they strive to make better decisions by finding good, or ideally the best (optimal), solutions to such problems. This module is concerned with how decision problems can be formulated mathematically and solved optimally to support the decision making process in organisations. The module will introduce several optimisation techniques and illustrate the application of these techniques on problems from different types of industries. The techniques introduced in this module have a wide range of applicability on decision problems arising in, among others, resource planning, machine scheduling, business investment, transportation, logistics and production planning.

This module will provide you with a sound foundation in the application of the many tools and techniques of management science. You are expected to learn the tools and the applications of modelling, optimization, computing and programming in solving practical problems drawn from many functional areas (operations, finance, marketing, and human resources, etc.) in different organizations (industry, finance, public sector, etc.).

This module will provide you with a sound foundation in the application of the many tools and techniques of management science. You are expected to learn the tools and the applications of modelling, optimization, computing and programming in solving practical problems drawn from many functional areas (operations, finance, marketing, and human resources, etc.) in different organizations (industry, finance, public sector, etc.).

This module will provide you with a sound foundation in the application of the many tools and techniques of management science. You are expected to learn the tools and the applications of modelling, optimization, computing and programming in solving practical problems drawn from many functional areas (operations, finance, marketing, and human resources, etc.) in different organizations (industry, finance, public sector, etc.).

This module is about the fundamentals of algorithms solving continuous optimisation problems, which involve minimising functions of multiple real-valued variables, possibly subject to restrictions, constraints, and nondifferentiable regularisations on the values that the variables may take. We focus (not exclusively) on convex optimisation, where the choice of topics is motivated by relevance to machine learning and data science.

The module has a two-part syllabus.

Part 1 covers the theoretical foundation of optimisation: convex analysis. Topics include the notion of convexity, subdifferential, optimality conditions and properties of various formulations of continuous optimisation problems.

Part 2 focuses on methods for solving optimisation problems. Topics include various gradient descent methods, higher-order methods, coordinate descent, randomisation, and heuristics.


[Module focus] This module is on structural continuous nonlinear optimisation in the real Euclidean space. This module is not about linear programming, combinatorial optimisation nor PDE-constrained optimisation.


[Prerequisites] A good knowledge of linear algebra and (differential) calculus is required for this module. Exposure to numerical analysis and vector calculus is helpful but not required; the applications will be kept basic and simple. Students will write scripts in MATLAB/Python, so familiarity with programming is required.


[Who should enrol] This module is expected to be beneficial to anyone who uses or will uses optimisation in machine learning and related work. More specifically, people from the following fields: machine learning, signal and image processing, communications, bioinformatics, control, robotics, computer graphics, computer vision, operation research, scientific computing, computational mathematics, and finance.

This module is about the fundamentals of algorithms solving continuous optimisation problems, which involve minimising functions of multiple real-valued variables, possibly subject to restrictions, constraints, and nondifferentiable regularisations on the values that the variables may take. We focus (not exclusively) on convex optimisation, where the choice of topics is motivated by relevance to machine learning and data science.

The module has a two-part syllabus.

Part 1 covers the theoretical foundation of optimisation: convex analysis. Topics include the notion of convexity, subdifferential, optimality conditions and properties of various formulations of continuous optimisation problems.

Part 2 focuses on methods for solving optimisation problems. Topics include various gradient descent methods, higher-order methods, coordinate descent, randomisation, and heuristics.


[Module focus] This module is on structural continuous nonlinear optimisation in the real Euclidean space. This module is not about linear programming, combinatorial optimisation nor PDE-constrained optimisation.


[Prerequisites] A good knowledge of linear algebra and (differential) calculus is required for this module. Exposure to numerical analysis and vector calculus is helpful but not required; the applications will be kept basic and simple. Students will write scripts in MATLAB/Python, so familiarity with programming is required.


[Who should enrol] This module is expected to be beneficial to anyone who uses or will uses optimisation in machine learning and related work. More specifically, people from the following fields: machine learning, signal and image processing, communications, bioinformatics, control, robotics, computer graphics, computer vision, operation research, scientific computing, computational mathematics, and finance.

This module is about the fundamentals of algorithms solving continuous optimisation problems, which involve minimising functions of multiple real-valued variables, possibly subject to restrictions, constraints, and nondifferentiable regularisations on the values that the variables may take. We focus (not exclusively) on convex optimisation, where the choice of topics is motivated by relevance to machine learning and data science.

The module has a two-part syllabus.

Part 1 covers the theoretical foundation of optimisation: convex analysis. Topics include the notion of convexity, subdifferential, optimality conditions and properties of various formulations of continuous optimisation problems.

Part 2 focuses on methods for solving optimisation problems. Topics include various gradient descent methods, higher-order methods, coordinate descent, randomisation, and heuristics.


[Module focus] This module is on structural continuous nonlinear optimisation in the real Euclidean space. This module is not about linear programming, combinatorial optimisation nor PDE-constrained optimisation.


[Prerequisites] A good knowledge of linear algebra and (differential) calculus is required for this module. Exposure to numerical analysis and vector calculus is helpful but not required; the applications will be kept basic and simple. Students will write scripts in MATLAB/Python, so familiarity with programming is required.


[Who should enrol] This module is expected to be beneficial to anyone who uses or will uses optimisation in machine learning and related work. More specifically, people from the following fields: machine learning, signal and image processing, communications, bioinformatics, control, robotics, computer graphics, computer vision, operation research, scientific computing, computational mathematics, and finance.

This module is about the fundamentals of algorithms solving continuous optimisation problems, which involve minimising functions of multiple real-valued variables, possibly subject to restrictions, constraints, and nondifferentiable regularisations on the values that the variables may take. We focus (not exclusively) on convex optimisation, where the choice of topics is motivated by relevance to machine learning and data science.

The module has a two-part syllabus.

Part 1 covers the theoretical foundation of optimisation: convex analysis. Topics include the notion of convexity, subdifferential, optimality conditions and properties of various formulations of continuous optimisation problems.

Part 2 focuses on methods for solving optimisation problems. Topics include various gradient descent methods, higher-order methods, coordinate descent, randomisation, and heuristics.


[Module focus] This module is on structural continuous nonlinear optimisation in the real Euclidean space. This module is not about linear programming, combinatorial optimisation nor PDE-constrained optimisation.


[Prerequisites] A good knowledge of linear algebra and (differential) calculus is required for this module. Exposure to numerical analysis and vector calculus is helpful but not required; the applications will be kept basic and simple. Students will write scripts in MATLAB/Python, so familiarity with programming is required.


[Who should enrol] This module is expected to be beneficial to anyone who uses or will uses optimisation in machine learning and related work. More specifically, people from the following fields: machine learning, signal and image processing, communications, bioinformatics, control, robotics, computer graphics, computer vision, operation research, scientific computing, computational mathematics, and finance.