Rigorous Error Bounds for the Optimal Value of Linear Programming Problems

2003 ◽  
pp. 59-70 ◽  
Author(s):  
Christian Jansson
Keyword(s):  
Linear Programming ◽  
Error Bounds ◽  
Rigorous Error Bounds ◽  
Optimal Value ◽  
Linear Programming Problems ◽  
Rigorous Error

scholarly journals Rigorous Error Bounds for the Optimal Value in Semidefinite Programming

2008 ◽  
Vol 46 (1) ◽  
pp. 180-200 ◽  
Author(s):  
Christian Jansson ◽  
Denis Chaykin ◽  
Christian Keil
Keyword(s):  
Semidefinite Programming ◽  
Error Bounds ◽  
Rigorous Error Bounds ◽  
Optimal Value ◽  
Rigorous Error

scholarly journals Tight and Rigorous Error Bounds for Basic Building Blocks of Double-Word Arithmetic

2017 ◽  
Vol 44 (2) ◽  
pp. 1-27 ◽  
Author(s):  
Mioara Joldes ◽  
Jean-Michel Muller ◽  
Valentina Popescu
Keyword(s):  
Error Bounds ◽  
Building Blocks ◽  
Rigorous Error Bounds ◽  
Rigorous Error

scholarly journals Robust estimation of risks from small samples

2017 ◽  
Vol 375 (2100) ◽  
pp. 20160299 ◽  
Author(s):  
Simon H. Tindemans ◽  
Goran Strbac
Keyword(s):  
Error Bounds ◽  
Probability Distributions ◽  
Simulated Data ◽  
Small Samples ◽  
Estimation Errors ◽  
Electricity Grid ◽  
Rigorous Error Bounds ◽  
Probability Mass ◽  
Rigorous Error ◽  
The Impact

Data-driven risk analysis involves the inference of probability distributions from measured or simulated data. In the case of a highly reliable system, such as the electricity grid, the amount of relevant data is often exceedingly limited, but the impact of estimation errors may be very large. This paper presents a robust non-parametric Bayesian method to infer possible underlying distributions. The method obtains rigorous error bounds even for small samples taken from ill-behaved distributions. The approach taken has a natural interpretation in terms of the intervals between ordered observations, where allocation of probability mass across intervals is well specified, but the location of that mass within each interval is unconstrained. This formulation gives rise to a straightforward computational resampling method: Bayesian interval sampling. In a comparison with common alternative approaches, it is shown to satisfy strict error bounds even for ill-behaved distributions. This article is part of the themed issue ‘Energy management: flexibility, risk and optimization’.

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