A novel inexact smoothing method is presented for solving the second-order cone complementarity problems (SOCCP). Our method reformulates the SOCCP as an equivalent nonlinear system of equations by introducing a regularized Chen-Harker-Kanzow-Smale smoothing function. At each iteration, Newton’s method is adopted to solve the system of equations approximately, which saves computation work compa...

This paper covers a massive acceleration of Monte-Carlo based pricing method for financial products and financial derivatives. The method is applicable in risk management settings, where a financial product has to be priced under a number of potential future scenarios. Instead of starting a separate nested Monte Carlo simulation for each scenario under consideration, the new method covers the u...

A method for reducing the cross-terms in the WD with an adaptive frequency smoothing window is proposed in this paper. By using the short time Fourier spectrum of the input signal and some simple preprocessing steps, such as smoothing and peak labeling, the proposed method adaptively calculates the width of the frequency smoothing window. The simulation results of the linear frequency modulated...

Widely used in speech and language processing, Kneser-Ney (KN) smoothing has consistently been shown to be one of the best-performing smoothing methods. However, KN smoothing assumes integer counts, limiting its potential uses—for example, inside Expectation-Maximization. In this paper, we propose a generalization of KN smoothing that operates on fractional counts, or, more precisely, on distri...

Smoothing with penalized splines calls for an automatic method to select the size of the penalty parameter λ . We propose a not well known smoothing parameter selection procedure: the L-curve method. AIC and (generalized) cross validation represent the most common choices in this kind of problems even if they indicate light smoothing when the data represent a smooth trend plus correlated noise....

Smoothing splines are a popular method for performing nonparametric regression. Most important in the implementation of this method is the choice of the smoothing parameter. This article provides a simulation study of several smoothing parameter selection methods, including two so{called risk estimation methods. To the best of the author's knowledge, the empirical performances of these two risk...

Another method of delayed reinforcement learning is proposed. There are two neural networks in a robot's brain, those are a evaluation network and a motion network. The evaluation network is trained so as to reduce the absolute value of the second order time derivative of the output of itself while the robot moves. The learning realize the evaluation by the necessary time until a robot gets a t...

Calibration estimation is currently the most popular method of estimation using auxiliary information. Its major idea is to use auxiliary information to structure calibration weights, attaching them to survey data, in order to improve the accuracy of the gross or mean estimation. Calibration estimation problem with box constraints is equivalently to solve a nonlinear equations system. Mnnich at...

The minimum sum-of-squares clustering problem is considered. The mathematical modeling of this problem leads to a min− sum−min formulation which, in addition to its intrinsic bi-level nature, has the significant characteristic of being strongly nondifferentiable. To overcome these difficulties, the resolution method proposed adopts a smoothing strategy using a special C differentiable class fun...