The weight is one of the main issues of Data Envelopment Analysis (DEA), and relevant theoretical research indicates that many DEA mathematical models include redundant restraints on weight, resulting in underestimated efficiency, pseudo inefficiency, and difficulty in representing specific Input/Output relationships. This study proposes a context-dependent DEA-R model to address shortcomings r...

This paper discusses the topic of dimensionality reduction for k-means clustering. We prove that any set of n points in d dimensions (rows in a matrix A ∈ R) can be projected into t = Ω(k/ε) dimensions, for any ε ∈ (0, 1/3), in O(nd⌈ε−2k/ log(d)⌉) time, such that with constant probability the optimal k-partition of the point set is preserved within a factor of 2 + ε. The projection is done by p...

Context-sensitive global analysis of large code bases can be expensive, which make its use impractical during software development. However, there are many situations in modifications small and isolated within a few components, it is desirable to reuse as much possible previous results. This has been achieved date through incremental fixpoint algorithms that achieve cost reductions at fine leve...