Suhrit Dey – författare

Perturbed functional iterations (PFI) is a large‑scale nonlinear system solver. Nature is abundant with events simulated mathematically by nonlinear systems of equations and inequalities. These we call nonlinear models. Often, they are ill‑conditioned, meaning small changes in data causing huge changes in the output. PFI, previously called the perturbed iterative scheme (PIS), is a numerical method to solve nonlinear systems of equations in multidimensional space. Computational results demonstrate that this numerical method has some unique features, which have made it more practical for applications in engineering and applied mathematics. This book will guide readers in the proper use of PFI, both in theoretical and practical settings.

Features:

Perturbed functional iterations (PFI) is a large‑scale nonlinear system solver. Nature is abundant with events simulated mathematically by nonlinear systems of equations and inequalities. These we call nonlinear models. Often, they are ill‑conditioned, meaning small changes in data causing huge changes in the output. PFI, previously called the perturbed iterative scheme (PIS), is a numerical method to solve nonlinear systems of equations in multidimensional space. Computational results demonstrate that this numerical method has some unique features, which have made it more practical for applications in engineering and applied mathematics. This book will guide readers in the proper use of PFI, both in theoretical and practical settings.

Features:

This book’s aim is to study the mathematical and computational models to analyze the progress, prognosis, prevention, and panacea of breast cancer. The book discusses application of Markov chains and transient mappings, Charlie–Simpson numerical algorithm, models represented by nonlinear reaction–diffusion-type partial differential equations, and related techniques. The book also attempts to design mathematical model of targeted strategic treatments by using Skilled Killer Drugs (SKD1 and SKD2) to suggest the improvisation of future cancer treatments. Both graduate students and researchers of computational biology and oncologists will benefit by studying this book. Researchers of cancer studies and biological sciences will also find this work helpful.