Random partitioning and adaptive filters for multiple-point stochastic simulation

Adaptive Random Testing Based on Two-Point Partitioning

Test data generation is a key issue in the field of software testing. Adaptive random testing (ART) method has been proposed by Chen et al. to improve the fault-revealing ability of random testing. In the paper, we are mainly concerned with the partitioning-based adaptive random testing and present a new ART based on two-point partitioning. In the new algorithm, the current max-area region is p...

Restricted Adaptive Random Testing by Random Partitioning

Adaptive Random Testing (ART) is designed to detect the first failure with fewer test cases than pure Random Testing. Since well-known ART methods, namely Distance-Based ART (D-ART) and Restriction-Based ART (RRT), have quadratic runtime, ART methods based on the idea of partitioning have been presented. ART by Random Partitioning is one of these partition-based ART algorithms. While having onl...

RSTREAM: STREAMS OF RANDOM NUMBERS FOR STOCHASTIC SIMULATION rstream: Streams of Random Numbers for Stochastic Simulation

R is an excellent tool for Steps 2 and 4 and it would be quite nice if it would provide better support for Step 3 as well. This is of particular interest for investigating complex models that go beyond the standard ones that are implemented as templates in simulation software. One requirement for this purpose is to have good sources (pseudo)random numbers available in the form of multiple strea...

RADAGRAD: Random Projections for Adaptive Stochastic Optimization

We present RADAGRAD a simple and computationally efficient approximation to full-matrix ADAGRAD based on dimensionality reduction using the subsampled randomized Hadamard transform. RADAGRAD is able to capture correlations in the gradients and achieves a similar regret – in theory and empirically – to fullmatrix ADAGRAD but at a computational cost comparable to the diagonal variant.

Random Process Simulation for Stochastic Fatigue Analysis