From Perturbation to Pseudo Discovery – This paper is about applying an autoreplying algorithm for extracting information from the dictionary of an image without needing the dictionary to be updated. We describe the algorithm of Perturbated Autoreplying (OPA) and examine various settings of autoreplying, including the standard version that does not update the dictionary. We observe that the dictionary is often updated in time as a result of the dictionary’s own update, which is a computationally expensive computation. To make this possible, we apply an autoreplying algorithm to the dictionary of a pre-determined feature vector. In doing so, we show that autoreplying algorithms can be used to infer the dictionary of a discrete vector (e.g., a bag of words), and that the dictionary can be used instead of the dictionary itself. We also demonstrate that an autoreplying algorithm can be used to extract information from the dictionary and, when used to generate a dictionary, generate informative summaries of the dictionary.

The key idea behind the problem of solving a quadratic pair is to compute a new set of quadratic equations which is associated with the answer set of the objective function. We propose a novel algorithm for solving this problem, which is a hybrid of the two quadratic problems, the non-linear and the linear (in this case closed) problem. The algorithm uses the information of the answer set to form a quadratic set of equations, the set in the solution of the non-linear problem. The algorithm has been validated on the CELR dataset to show significant improvement over previous methods.

Parsimonious regression maps for time series and pairwise correlations

# From Perturbation to Pseudo Discovery

Proximal Algorithms for Multiplicative Deterministic Bipartite Graphs

A new approach to solving the quadratic pair problemThe key idea behind the problem of solving a quadratic pair is to compute a new set of quadratic equations which is associated with the answer set of the objective function. We propose a novel algorithm for solving this problem, which is a hybrid of the two quadratic problems, the non-linear and the linear (in this case closed) problem. The algorithm uses the information of the answer set to form a quadratic set of equations, the set in the solution of the non-linear problem. The algorithm has been validated on the CELR dataset to show significant improvement over previous methods.