We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Durham University

Research & business

View Profile

Publication details for Camila Caiado

Nakharutai, Nawapon, Troffaes, Matthias C. M. & Caiado, Camila C. C. S. (2017), Efficient algorithms for checking avoiding sure loss, in Antonucci, Alessandro, Corani, Giorgio, Couso, Inés & Destercke, Sébastien eds, Proceedings of Machine Learning Research 62: The Tenth International Symposium on Imprecise Probability: Theories and Applications (ISIPTA ’17). Lugano, Switzerland, PMLR, 241-252.

Author(s) from Durham

Other versions of this publication


Sets of desirable gambles provide a general representation of uncertainty which can handle partial information in a more robust way than precise probabilities. Here we study the effectiveness of linear programming algorithms for determining whether or not a given set of desirable gambles avoids sure loss (i.e. is consistent). We also suggest improvements to these algorithms specifically for checking avoiding sure loss. By exploiting the structure of the problem, (i) we slightly reduce its dimension, (ii) we propose an extra stopping criterion based on its degenerate structure, and (iii) we show that one can directly calculate feasible starting points in various cases, therefore reducing the effort required in the presolve phase of some of these algorithms. To assess our results, we compare the impact of these improvements on the simplex method and two interior point methods (affine scaling and primal-dual) on randomly generated sets of desirable gambles that either avoid or do not avoid sure loss. We find that the simplex method is outperformed by the primal-dual and affine scaling methods, except for very small problems. We also find that using our starting feasible point and extra stopping criterion considerably improves the performance of the primal-dual and affine scaling methods.