Abstract: The tensor train is a popular model for approximating high-dimensional rectangular data structures that cannot fit in any computer memory due to their size. The tensor train can approximate ...
LINEAR-QUADRATIC CONTROL FOR A CLASS OF STOCHASTIC VOLTERRA EQUATIONS: SOLVABILITY AND APPROXIMATION
The Annals of Applied Probability, Vol. 31, No. 5 (October 2021), pp. 2244-2274 (31 pages) We provide an exhaustive treatment of linear-quadratic control problems for a class of stochastic Volterra ...
Algorithm 1 Rendering of the Approximate Stroke Region: quadratic.geom for triangles generation; quadratic.frag for implicit-equation based rendering. Algorithm 2 Curvature-Guided Adaptive Subdivision ...
The problem of approximation to a given function, or of fitting a given set of data, where the approximating function is required to have certain of its derivatives of specified sign over the whole ...
Every day, researchers search for optimal solutions. They might want to figure out where to build a major airline hub. Or to determine how to maximize return while minimizing risk in an investment ...
1 Institute of Mathematics, University of Lübeck, Lübeck, Germany 2 Institute of Mathematics, National Academy of Sciences of Ukraine, Kyiv, Ukraine This paper ...
This work explores the representation of univariate and multivariate functions as matrix product states (MPS), also known as quantized tensor-trains (QTT). It proposes an algorithm that employs ...
ABSTRACT: In this paper, we modify the Bregman APGs (BAPGs) method proposed in (Wang, L, et al.) for solving the support vector machine problem with truncated loss (HTPSVM) given in (Zhu, W, et al.), ...
Objectives: 1) Design a KNN algorithm using R, 2) Summarize classification performance using KNN, linear/quadratic regression and Bayes rule. Training and test data are generated from a bi-variate ...
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