Iterative Methods for Systems of Equations
... Notice that at each iteration the first thing we do is get a new approximation for x1 and then we continue to use the old approximation to x1 in subsequent calculations for that iteration! Only at the next iteration do we use the new value. Similarly, we continue to use an old approximation to x2 ev ...
... Notice that at each iteration the first thing we do is get a new approximation for x1 and then we continue to use the old approximation to x1 in subsequent calculations for that iteration! Only at the next iteration do we use the new value. Similarly, we continue to use an old approximation to x2 ev ...
QUANTUM – TYPE AND CONTINUOUS COMPRESSIONS Author:I. Szalay
... x c is called the c –compressed of x and R c is called the c- compressed of the real number line. If S R then the set S c x c R c : x S is called the c – compressed of S. The inverse of compressor ...
... x c is called the c –compressed of x and R c is called the c- compressed of the real number line. If S R then the set S c x c R c : x S is called the c – compressed of S. The inverse of compressor ...
... Our goal is to find the optimal shape of the transistor width function. This class of problems belongs to the domain of variational calculus in mathematics. Therefore, we will first introduce the fundamental equation in the variational calculus: the Euler–Lagrange differential equation. If a cost fu ...
Dual characterization of properties of risk measures on Orlicz hearts
... The purpose of this paper is to give characterizations of properties of risk measures that can be used to analyze particular examples. We first extend earlier representation results for risk measures on Orlicz hearts. Then we give general conditions for monetary risk measures to be Gâteaux-differen ...
... The purpose of this paper is to give characterizations of properties of risk measures that can be used to analyze particular examples. We first extend earlier representation results for risk measures on Orlicz hearts. Then we give general conditions for monetary risk measures to be Gâteaux-differen ...
Mathematical optimization
In mathematics, computer science and operations research, mathematical optimization (alternatively, optimization or mathematical programming) is the selection of a best element (with regard to some criteria) from some set of available alternatives.In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics. More generally, optimization includes finding ""best available"" values of some objective function given a defined domain (or a set of constraints), including a variety of different types of objective functions and different types of domains.