Least-squares optimized polynomials are discussed which are needed in the twostep multi-bosonic algorithm for Monte Carlo simulations of quantum field theories with fermions. A recurrence scheme for the calculation of necessary coefficients in the recursion and for the evaluation of these polynomials is introduced.

Least-squares optimized polynomials are discussed which are needed in the twostep multi-bosonic algorithm for Monte Carlo simulations of quantum field theories with fermions. A recurrence scheme for the calculation of necessary coefficients in the recursion and for the evaluation of these polynomials is introduced.

Telecommunication Companies use data mining techniques to maintain good relationships with their existing customers and attract new customers and identifying profitable/unprofitable customers. Clustering leads to better understanding of customer and its results can be used to definition and decision-making for promotional schemes. In this study, we used the 999-customer purchase records in Sout...

We propose a novel algorithm for solving the Traveling Salesman Problem (TSP). Our approach uses Genetic Algorithm as a guiding tool to direct Monte Carlo search towards convergence. This hybrid approach leverages a Genetic Algorithm to generate individuals with highly fit genes and uses that information as a base plan for Monte Carlo search. Our experiments suggest that this twostep Expectatio...

We present a novel model identification technique for designing complex infinite-impulseresponse (IIR) continuous-time filters through generalizing the Vector Fitting (VF) algorithm, which is extensively used for continuous-time frequencydomain rational approximation to symmetric functions, to asymmetrical cases. VF involves a twostep pole refinement process based on a linear leastsquares solve...

In this paper, we propose a fast Gauss-Seidel Operator Splitting (GSOS) algorithm for addressing multi-term nonsmooth convex composite optimization, which has wide applications in machine learning, signal processing and statistics. The proposed GSOS algorithm inherits the advantage of the Gauss-Seidel technique to accelerate the optimization procedure, and leverages the operator splitting techn...

In this paper we analyze information about the customers of a bank, dividing them into three clusters, using SPSS TwoStep Cluster method. This method is perfect for our case study, because, compared to other classical clustering methods, TwoStep uses mixture data (both continuous and categorical variables) and it also finds the optimal number of clusters. TwoStep creates three customers’ profil...

In this paper, we describe our approaches in the NTCIR12 IMine task, including Chinese Query Understanding and Chinese Vertical Incorporating. In Query Understanding subtask, we propose different strategies to mine subtopic candidates from a wide range of resources and present a twostep method to predict the vertical intent for each subtopic. In Vertical Incorporating subtask, we adopt a probab...

We introduce a semi-supervised boosting approach (SSDBoost), which directly minimizes the classification errors and maximizes the margins on both labeled and unlabeled samples, without resorting to any upper bounds or approximations. A twostep algorithm based on coordinate descent/ascent is proposed to implement SSDBoost. Experiments on a number of UCI datasets and synthetic data show that SSDB...