Florida International University
Pace University
Portland State University
University of Minnesota, Twin Cities
Rose-Hulman Institute of Technology
School of Mathematics and Statistics, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH, England
Southern Methodist University
California State University, San Bernardino
Colorado State University-Pueblo
Non-Nested Monte Carlo Dual Bounds For Multi-Exercisable Options,
2019
Centre for Actuarial Studies, Department of Economics, The University of Melbourne, VIC 3010, Australia
Non-Nested Monte Carlo Dual Bounds For Multi-Exercisable Options, Xiang Cheng, Zhuo Jin
Communications on Stochastic Analysis
No abstract provided.
Hybrid Models And Switching Control With Constraints,
2019
Wayne State University
Hybrid Models And Switching Control With Constraints, Jose L. Menaldi, Maurice Robin
Communications on Stochastic Analysis
No abstract provided.
Totalitarian Random Tug-Of-War Games In Graphs,
2019
Universitat Politècnica de Catalunya, Departament de Matemàtiques, Diagonal 647, 08028 Barcelona, Spain
Totalitarian Random Tug-Of-War Games In Graphs, Marcos Antón, Fernando Charro, Peiyong Wang
Communications on Stochastic Analysis
No abstract provided.
Stochastic Process And Its Role In The Development Of The Financial Market: Celebrating Professor Chow's Long And Successful Career,
2019
18 Thoroughbred Drive, Sherborn, MA 01770, USA
Stochastic Process And Its Role In The Development Of The Financial Market: Celebrating Professor Chow's Long And Successful Career, Xisuo L. Liu
Communications on Stochastic Analysis
No abstract provided.
Preface,
2019
Louisiana State University, Baton Rouge, LA 70803 USA
Preface, Hui-Hsiung Kuo, George Yin
Communications on Stochastic Analysis
No abstract provided.
Subdifferentials Of Value Functions In Nonconvex Dynamic Programming For Nonstationary Stochastic Processes,
2019
Department of Mathematics, Wayne State University, Detroit, MI 48202, USA
Subdifferentials Of Value Functions In Nonconvex Dynamic Programming For Nonstationary Stochastic Processes, Boris S. Mordukhovich, Nobusumi Sagara
Communications on Stochastic Analysis
No abstract provided.
Euler-Maruyama Method For Regime Switching Stochastic Differential Equations With Hölder Coefficients,
2019
Department of Applied Mathematics, Faculty of Applied Science, HCMC University of Technology, Vietnam
Euler-Maruyama Method For Regime Switching Stochastic Differential Equations With Hölder Coefficients, Dung T. Nguyen, Son L. Nguyen
Communications on Stochastic Analysis
No abstract provided.
Maps Preserving Norms Of Generalized Weighted Quasi-Arithmetic Means Of Invertible Positive Operators,
2019
Institute of Mathematics, University of Debrecen, H-4002 Debrecen, P.O. Box 400
Maps Preserving Norms Of Generalized Weighted Quasi-Arithmetic Means Of Invertible Positive Operators, Gergő Nagy, Patricia Szokol
Electronic Journal of Linear Algebra
In this paper, the problem of describing the structure of transformations leaving norms of generalized weighted quasi-arithmetic means of invertible positive operators invariant is discussed. In a former result of the authors, this problem was solved for weighted quasi-arithmetic means, and here the corresponding result is generalized by establishing its solution under certain mild conditions. It is proved that in a quite general setting, generalized weighted quasi-arithmetic means on self-adjoint operators are not monotone in their variables which is an interesting property. Moreover, the relation of these means with the Kubo-Ando means is investigated and it is shown that the ...
Analysis Of Feast Spectral Approximations Using The Dpg Discretization,
2019
Portland State University
Analysis Of Feast Spectral Approximations Using The Dpg Discretization, Jay Gopalakrishnan, Luka Grubišić, Jeffrey S. Ovall, Benjamin Q. Parker
Jeffrey S. Ovall
A filtered subspace iteration for computing a cluster of eigenvalues and its accompanying eigenspace, known as “FEAST”, has gained considerable attention in recent years. This work studies issues that arise when FEAST is applied to compute part of the spectrum of an unbounded partial differential operator. Specifically, when the resolvent of the partial differential operator is approximated by the discontinuous Petrov Galerkin (DPG) method, it is shown that there is no spectral pollution. The theory also provides bounds on the discretization errors in the spectral approximations. Numerical experiments for simple operators illustrate the theory and also indicate the value of ...
Polynomial And Rational Convexity Of Submanifolds Of Euclidean Complex Space,
2019
The University of Western Ontario
Polynomial And Rational Convexity Of Submanifolds Of Euclidean Complex Space, Octavian Mitrea
Electronic Thesis and Dissertation Repository
The goal of this dissertation is to prove two results which are essentially independent, but which do connect to each other via their direct applications to approximation theory, symplectic geometry, topology and Banach algebras. First we show that every smooth totally real compact surface in complex Euclidean space of dimension 2 with finitely many isolated singular points of the open Whitney umbrella type is locally polynomially convex. The second result is a characterization of the rational convexity of a general class of totally real compact immersions in complex Euclidean space of dimension n..
Analysis Of Feast Spectral Approximations Using The Dpg Discretization,
2019
Portland State University
Analysis Of Feast Spectral Approximations Using The Dpg Discretization, Jay Gopalakrishnan, Luka Grubišić, Jeffrey S. Ovall, Benjamin Q. Parker
Jay Gopalakrishnan
A filtered subspace iteration for computing a cluster of eigenvalues and its accompanying eigenspace, known as “FEAST”, has gained considerable attention in recent years. This work studies issues that arise when FEAST is applied to compute part of the spectrum of an unbounded partial differential operator. Specifically, when the resolvent of the partial differential operator is approximated by the discontinuous Petrov Galerkin (DPG) method, it is shown that there is no spectral pollution. The theory also provides bounds on the discretization errors in the spectral approximations. Numerical experiments for simple operators illustrate the theory and also indicate the value of ...
General Nonlinear-Material Elasticity In Classical One-Dimensional Solid Mechanics,
2019
University of New Orleans
General Nonlinear-Material Elasticity In Classical One-Dimensional Solid Mechanics, Ronald Joseph Giardina Jr
University of New Orleans Theses and Dissertations
We will create a class of generalized ellipses and explore their ability to define a distance on a space and generate continuous, periodic functions. Connections between these continuous, periodic functions and the generalizations of trigonometric functions known in the literature shall be established along with connections between these generalized ellipses and some spectrahedral projections onto the plane, more specifically the well-known multifocal ellipses. The superellipse, or Lam\'{e} curve, will be a special case of the generalized ellipse. Applications of these generalized ellipses shall be explored with regards to some one-dimensional systems of classical mechanics. We will adopt the Ramberg-Osgood ...
Understanding Volume Transport In The Jordan River: An Application Of The Navier-Stokes Equations,
2019
University of Maine
Understanding Volume Transport In The Jordan River: An Application Of The Navier-Stokes Equations, Gwyneth E. Roberts
Honors College
This study aims to characterize the circulation patterns in short and narrow estuarine systems on various temporal scales to identify the controls of material transport. In order to achieve this goal, a combination of in situ collected data and analytical modeling was used. The model is based on the horizontal Reynolds Averaged Navier-Stokes equations in the shallow water limit with scaling parameters defined from the characteristics of the estuary. The in situ measurements were used to inform a case study, seeking to understand water level variations and tidal current velocity patterns in the Jordan River and to improve understanding of ...
Predictive Diagnostic Analysis Of Mammographic Breast Tissue Microenvironment,
2019
University of Maine
Predictive Diagnostic Analysis Of Mammographic Breast Tissue Microenvironment, Dexter G. Canning
Honors College
Improving computer-aided early detection techniques for breast cancer is paramount because current technology has high false positive rates. Existing methods have led to a substantial number of false diagnostics, which lead to stress, unnecessary biopsies, and an added financial burden to the health care system. In order to augment early detection methodology, one must understand the breast microenvironment. The CompuMAINE Lab has researched computational metrics on mammograms based on an image analysis technique called the Wavelet Transform Modulus Maxima (WTMM) method to identify the fractal and roughness signature from mammograms. The WTMM method was used to color code the mammograms ...
Krylov Subspace Spectral Methods With Non-Homogenous Boundary Conditions,
2019
The University of Southern Mississippi
Krylov Subspace Spectral Methods With Non-Homogenous Boundary Conditions, Abbie Hendley
Master's Theses
For this thesis, Krylov Subspace Spectral (KSS) methods, developed by Dr. James Lambers, will be used to solve a one-dimensional, heat equation with non-homogenous boundary conditions. While current methods such as Finite Difference are able to carry out these computations efficiently, their accuracy and scalability can be improved. We will solve the heat equation in one-dimension with two cases to observe the behaviors of the errors using KSS methods. The first case will implement KSS methods with trigonometric initial conditions, then another case where the initial conditions are polynomial functions. We will also look at both the time-independent and time-dependent ...
Approximation Of Continuous Functions By Artificial Neural Networks,
2019
Union College - Schenectady, NY
Approximation Of Continuous Functions By Artificial Neural Networks, Zongliang Ji
Honors Theses
An artificial neural network is a biologically-inspired system that can be trained to perform computations. Recently, techniques from machine learning have trained neural networks to perform a variety of tasks. It can be shown that any continuous function can be approximated by an artificial neural network with arbitrary precision. This is known as the universal approximation theorem. In this thesis, we will introduce neural networks and one of the first versions of this theorem, due to Cybenko. He modeled artificial neural networks using sigmoidal functions and used tools from measure theory and functional analysis.
Increasing C-Additive Processes,
2019
Department of Mathematical Sciences, University of Indianapolis, Indianapolis, IN 46627, USA
Increasing C-Additive Processes, Nadjib Bouzar
Communications on Stochastic Analysis
No abstract provided.
Strong Convergence Rate In Averaging Principle For The Heat Equation Driven By A General Stochastic Measure,
2019
Department of Mathematical Analysis, Taras Shevchenko National University of Kyiv, Kyiv 01601, Ukraine
Strong Convergence Rate In Averaging Principle For The Heat Equation Driven By A General Stochastic Measure, Vadym Radchenko
Communications on Stochastic Analysis
No abstract provided.
Smoothing Parameters For Recursive Kernel Density Estimators Under Censoring,
2019
Univ. Poitiers, Lab. Math. et Appl., Futuroscope Chasseneuil, France
Smoothing Parameters For Recursive Kernel Density Estimators Under Censoring, Yousri Slaoui
Communications on Stochastic Analysis
No abstract provided.
Spectral Theorem Approach To The Characteristic Function Of Quantum Observables,
2019
Universitá di Roma Tor Vergata, Via di Torvergata, Roma, Italy
Spectral Theorem Approach To The Characteristic Function Of Quantum Observables, Andreas Boukas, Philip J. Feinsilver
Communications on Stochastic Analysis
No abstract provided.
