We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. The resulting optimisation problem is then tackled using constrained gradient optimisation. Our powerful decoding framework, enables decoding intractable models such as the intersection of left-to-right and rightto-left (bidirectional) as well as sourceto-target and target-to-source (bili...

The evolutionary approach in the design optimisation of MEMS is a novel and promising research area. The problem is of a multi-objective nature; hence, multi-objective evolutionary algorithms (MOEA) are used. The literature shows that two main classes of MOEA have been used in MEMS evolutionary design Optimisation, NSGA-II and MOGA-II. However, no one has provided a justification for using eith...

This thesis combines average-case complexity theory with the approximability of optimisation problems. Both are ways of dealing with the fact that many computational problems are not solvable in polynomial time, unless P = NP. A theoretical framework is established that allows both the classification of optimisation problems with respect to their average-case approximability and the study of th...

A Hysys model representing the steady state behaviour of a Haber-Bosch ammonia plant was developed based on a dynamic model and plant data from Norwegian fertiliser company Yara International ASA. The plant model was optimised using matlabs fmincon function with an interior-point method for economical optimisation with the aim to study the performance of different strategies. The Haber-Bosch am...

Although many methods for dealing with multi-objective optimisation (MOO) problems are available [Deb01] and successful applications have been reported [Coe01], the comparison between MOO methods applied to real-world problem was rarely carried out. This paper reports the comparison between MOO methods applied to a real-world problem, namely, the optimisation of a micro heat exchanger ( HEX). T...

Constructive methods for parallel programming are characterised by the composition of optimised, parallel software components. This paper concerns data placement, a key cross-component optimisation for regular data-parallel programs. This article is a survey of data placement optimisation techniques. The main contributions are (1) a uniform terminology, which identifies analyses of the problem ...

This paper describes the results of initial experiments to apply computational algorithms to explore a large parameter space containingmany variables in the search for an optimal solution for the sustainable design of an urban development using a potentially complicated fitness function. This initial work concentrates on varying the placement of buildings to optimise solar irradiation availabil...

Numerical weather predicting models often require solving a 3-D Helmholtz problem which derived from the governing equation of dynamical core in Met Office Unified Model, by preconditioned iterative solvers. In this dissertation, a GPU implementation of preconditioned conjugate gradient (CG) iterative method will be focused on. A given serial code has been ported on GPU. According to the portin...

Multilevel algorithms are a successful class of optimisation techniqueswhich address themeshpartitioning problem. They usually combine a graph contraction algorithm together with a local optimisation method which refines the partition at each graph level. In this paper we present an enhancement of the technique which uses imbalance to achieve higher quality partitions. We also present a formula...

Multi-objective optimisation is a proven, well-known parameter tuning technique in control design. It is especially suited to solve complex, multi-disciplinary design problems. This paper describes a software environment, called MOPS (Multi-Objective Parameter Synthesis), which supports the control engineer in setting up his design problem as a properly formulated multi-objective optimisation t...