Experiences with stochastic algorithms for a class of constrained global optimisation problems

The solution of a variety of classes of global optimisation problems is required in the implementation of a framework for sensitivity analysis in multicriteria decision analysis. These problems have linear constraints, some of which have a particular structure, and a variety of objective functions, which may be smooth or non-smooth. The context in which they arise implies a need for a single, robust solution method. The literature contains few experimental results relevant to such a need. We report on our experience with the implementation of three stochastic algorithms for global optimisation: the multi-level single linkage algorithm, the topographical algorithm and the simulated annealing algorithm. Issues relating to their implementation and use to solve practical problems are discussed. Computational results suggest that, for the class of problems considered, simulated annealing performs well

Gradient Methods for Solving Stackelberg Games

Stackelberg Games are gaining importance in the last years due to the raise of Adversarial Machine Learning (AML). Within this context, a new paradigm must be faced: in classical game theory, intervening agents were humans whose decisions are generally discrete and low dimensional. In AML, decisions are made by algorithms and are usually continuous and high dimensional, e.g. choosing the weights of a neural network. As closed form solutions for Stackelberg games generally do not exist, it is mandatory to have efficient algorithms to search for numerical solutions. We study two different procedures for solving this type of games using gradient methods. We study time and space scalability of both approaches and discuss in which situation it is more appropriate to use each of them. Finally, we illustrate their use in an adversarial prediction problem.Comment: Accepted in ADT Conference 201

Challenge 8: Smart Cybersecurity

Peer reviewe

A Review and Classification of Approaches for Dealing with Uncertainty in Multi-Criteria Decision Analysis for Healthcare Decisions

The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Multi-criteria decision analysis (MCDA) is increasingly used to support decisions in healthcare involving multiple and conflicting criteria. Although uncertainty is usually carefully addressed in health eco-nomic evaluations, whether and how the different sources of uncertainty are dealt with and with what methods in MCDA is less known. The objective of this study is to review how uncertainty can be explicitly taken into account in MCDA and to discuss which approach may be appro-priate for healthcare decision makers. A literature review was conducted in the Scopus and PubMed databases. Two reviewers independently categorized studies according to research areas, the type of MCDA used, and the approach used to quantify uncertainty. Selected full text articles wer