background preloader

Methods in math

Facebook Twitter

Laplacian of the indicator. In mathematics, the Laplacian of the indicator of the domain D is a generalisation of the derivative of the Dirac delta function to higher dimensions, and is non-zero only on the surface of D. It can be viewed as the surface delta prime function. It is analogous to the second derivative of the Heaviside step function in one dimension. It can be obtained by letting the Laplace operator work on the indicator function of some domain D. History[edit] An approximation of the negative indicator function of an ellipse in the plane (left), the derivative in the direction normal to the boundary (middle), and its Laplacian (right). In the limit, the right-most graph goes to the (negative) Laplacian of the indicator. Paul Dirac introduced the Dirac δ-function, as it has become known, as early as 1930.[1] The one-dimensional Dirac δ-function is non-zero only at a single point.

However, a different generalisation is possible. Dirac surface delta prime function[edit] Dirac surface delta function[edit] The Maya way of forming a right angle. The great Mayan pyramid of Kukulcan "El Castillo" as seen from the Platform of the Eagles and Jaguars, Chichen Itza, Mexico. How do you construct a right angle when you haven't got a way of measuring angles? One very clever way comes from the Mayan people. The classic Maya period ran roughly from 250 to 900 AD. During that time the Maya constructed hundreds of cities in an area that stretches from what is now southern Mexico across the Yucatan Peninsula to western Honduras and El Salvador, including what is now Guatemala and Belize. In a lecture during the 2011 MAA Study Tour, Powell explained that he had heard about the technique from a master builder who had learned it while a shaman apprentice. Since the knots are evenly spaced, when knots 1 and 4 are held together and the cord pulled taut, an equilateral triangle with interior angles of 60° is formed.

John C. About this article John C. Cynthia J. Discrete event simulation. This contrasts with continuous simulation in which the simulation continuously tracks the system dynamics over time. Instead of being event-based, this is called an activity-based simulation; time is broken up into small time slices and the system state is updated according to the set of activities happening in the time slice.[2] Because discrete-event simulations do not have to simulate every time slice, they can typically run much faster than the corresponding continuous simulation. A more recent method is the three-phased approach to discrete event simulation (Pidd, 1998).

In this approach, the first phase is to jump to the next chronological event. The second phase is to execute all events that unconditionally occur at that time (these are called B-events). The third phase is to execute all events that conditionally occur at that time (these are called C-events). Example[edit] Components of a discrete-event simulation[edit] State[edit] Clock[edit] Events list[edit] Statistics[edit]

Euler method. Illustration of the Euler method. The unknown curve is in blue, and its polygonal approximation is in red. In mathematics and computational science, the Euler method is a SN-order[jargon] numerical procedure for solving ordinary differential equations (ODEs) with a given initial value. It is the most basic explicit method for numerical integration of ordinary differential equations and is the simplest Runge–Kutta method. The Euler method is named after Leonhard Euler, who treated it in his book Institutionum calculi integralis (published 1768–70).[1] The Euler method is a first-order method, which means that the local error (error per step) is proportional to the square of the step size, and the global error (error at a given time) is proportional to the step size.

The Euler method often serves as the basis to construct more complex methods. Informal geometrical description[edit] The idea is that while the curve is initially unknown, its starting point, which we denote by is computed. . To. List of Runge–Kutta methods. Runge–Kutta methods are methods for the numerical solution of the ordinary differential equation which take the form The methods listed on this page are each defined by its Butcher tableau, which puts the coefficients of the method in a table as follows: Explicit methods[edit] The explicit methods are those where the matrix is lower triangular.

Forward Euler[edit] The Euler method is first order. Explicit midpoint method[edit] The (explicit) midpoint method is a second-order method with two stages (see also the implicit midpoint method below): Heun's method[edit] Heun's method is a second-order method with two stages (also known as explicit trapezoid rule): Ralston's method[edit] Ralston's method is a second-order method with two stages and a minimum local error bound: Generic second-order method[edit] Kutta's third-order method[edit] Classic fourth-order method[edit] The "original" Runge–Kutta method. 3/8-rule fourth-order method[edit] Embedded methods[edit] The lower-order step is given by where the.

Dynamic errors of numerical methods of ODE discretization. The dynamical characteristic of the numerical method of ordinary differential equations (ODE) discretization – is the natural logarithm of its function of stability . Dynamic characteristic is considered in three forms: – Complex dynamic characteristic; – Real dynamic characteristics; – Imaginary dynamic characteristics. The dynamic characteristic represents the transformation operator of eigenvalues of a Jacobian matrix of the initial differential mathematical model (MM) in eigenvalues of a Jacobian matrix of mathematical model (also differential) whose exact solution passes through the discrete sequence of points of the initial MM solution received by given numerical method.

See also[edit] References[edit] Kosteltsev V.I. External links[edit] Dynamic properties and characteristics of RK-methods. General linear methods. General linear methods (GLMs) are a large class of numerical methods used to obtain numerical solutions to differential equations. This large class of methods in numerical analysis encompass multistage Runge–Kutta methods that use intermediate collocation points, as well as linear multistep methods that save a finite time history of the solution.

John C. Butcher originally coined this term for these methods, and has written a series of review papers[1] [2] [3] a book chapter[4] and a textbook[5] on the topic. His collaborator, Zdzislaw Jackiewicz also has an extensive textbook[6] on the topic. Some definitions[edit] Numerical methods for first-order ordinary differential equations approximate solutions to initial value problems of the form The result is approximations for the value of at discrete times where h is the time step (sometimes referred to as A description of the method[edit] General linear methods make use of two integers, , the number of time points in history and Stage values and.

Runge–Kutta methods. In numerical analysis, the Runge–Kutta methods are an important family of implicit and explicit iterative methods, which are used in temporal discretization for the approximation of solutions of ordinary differential equations. These techniques were developed around 1900 by the German mathematicians C. Runge and M. W. Kutta. See the article on numerical methods for ordinary differential equations for more background and other methods. The Runge–Kutta method[edit] One member of the family of Runge–Kutta methods is often referred to as "RK4", "classical Runge–Kutta method" or simply as "the Runge–Kutta method". Let an initial value problem be specified as follows. Here, y is an unknown function (scalar or vector) of time t which we would like to approximate; we are told that , the rate at which y changes, is a function of t and of y itself. The corresponding y-value is .

Are given. Now pick a step-size h>0 and define for n = 0, 1, 2, 3, . . . , using Here is the RK4 approximation of where , then . . Monte Carlo method. Probabilistic problem-solving algorithm Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches.

Monte Carlo methods are mainly used in three problem classes:[1] optimization, numerical integration, and generating draws from a probability distribution. In principle, Monte Carlo methods can be used to solve any problem having a probabilistic interpretation. By the law of large numbers, integrals described by the expected value of some random variable can be approximated by taking the empirical mean (a.k.a. the sample mean) of independent samples of the variable. Overview[edit] Monte Carlo method applied to approximating the value of π. History[edit] Linear multistep method.

Linear multistep methods are used for the numerical solution of ordinary differential equations. Conceptually, a numerical method starts from an initial point and then takes a short step forward in time to find the next solution point. The process continues with subsequent steps to map out the solution. Single-step methods (such as Euler's method) refer to only one previous point and its derivative to determine the current value. Methods such as Runge–Kutta take some intermediate steps (for example, a half-step) to obtain a higher order method, but then discard all previous information before taking a second step.

Multistep methods attempt to gain efficiency by keeping and using the information from previous steps rather than discarding it. Consequently, multistep methods refer to several previous points and derivative values. Definitions[edit] Numerical methods for ordinary differential equations approximate solutions to initial value problems of the form at discrete times where ) and and.