Formula Development Through Finite Differences
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Chapter 4 Finite Difference Gradient Information
Chapter 4 Finite Difference Gradient Information In the masters thesis Thoresson (2003), the feasibility of using gradient- based approximation methods for the optimisation of the spring and damper characteristics of an off-road vehicle, for both ride comfort and handling, was investigated. The Sequential Quadratic Programming (SQP) algorithm and the locally developed Dynamic-Q method were the two successive approximation methods used for the optimisation. The determination of the objective function value is performed using computationally expensive numerical simulations that exhibit severe inherent numerical noise. The use of forward finite differences and central finite differences for the determination of the gradients of the objective function within Dynamic-Q is also investigated. The results of this study, presented here, proved that the use of central finite differencing for gradient information improved the optimisation convergence history, and helped to reduce the difficulties associated with noise in the objective and constraint functions. This chapter presents the feasibility investigation of using gradient-based successive approximation methods to overcome the problems of poor gradient information due to severe numerical noise. The two approximation methods applied here are the locally developed Dynamic-Q optimisation algorithm of Snyman and Hay (2002) and the well known Sequential Quadratic 47 CHAPTER 4. FINITE DIFFERENCE GRADIENT INFORMATION 48 Programming (SQP) algorithm, the MATLAB implementation being used for this research (Mathworks 2000b). This chapter aims to provide the reader with more information regarding the Dynamic-Q successive approximation algorithm, that may be used as an alternative to the more established SQP method. Both algorithms are evaluated for effectiveness and efficiency in determining optimal spring and damper characteristics for both ride comfort and handling of a vehicle. -
Methods of Measuring Population Change
Methods of Measuring Population Change CDC 103 – Lecture 10 – April 22, 2012 Measuring Population Change • If past trends in population growth can be expressed in a mathematical model, there would be a solid justification for making projections on the basis of that model. • Demographers developed an array of models to measure population growth; four of these models are usually utilized. 1 Measuring Population Change Arithmetic (Linear), Geometric, Exponential, and Logistic. Arithmetic Change • A population growing arithmetically would increase by a constant number of people in each period. • If a population of 5000 grows by 100 annually, its size over successive years will be: 5100, 5200, 5300, . • Hence, the growth rate can be calculated by the following formula: • (100/5000 = 0.02 or 2 per cent). 2 Arithmetic Change • Arithmetic growth is the same as the ‘simple interest’, whereby interest is paid only on the initial sum deposited, the principal, rather than on accumulating savings. • Five percent simple interest on $100 merely returns a constant $5 interest every year. • Hence, arithmetic change produces a linear trend in population growth – following a straight line rather than a curve. Arithmetic Change 3 Arithmetic Change • The arithmetic growth rate is expressed by the following equation: Geometric Change • Geometric population growth is the same as the growth of a bank balance receiving compound interest. • According to this, the interest is calculated each year with reference to the principal plus previous interest payments, thereby yielding a far greater return over time than simple interest. • The geometric growth rate in demography is calculated using the ‘compound interest formula’. -
Math Club: Recurrent Sequences Nov 15, 2020
MATH CLUB: RECURRENT SEQUENCES NOV 15, 2020 In many problems, a sequence is defined using a recurrence relation, i.e. the next term is defined using the previous terms. By far the most famous of these is the Fibonacci sequence: (1) F0 = 0;F1 = F2 = 1;Fn+1 = Fn + Fn−1 The first several terms of this sequence are below: 0; 1; 1; 2; 3; 5; 8; 13; 21; 34;::: For such sequences there is a method of finding a general formula for nth term, outlined in problem 6 below. 1. Into how many regions do n lines divide the plane? It is assumed that no two lines are parallel, and no three lines intersect at a point. Hint: denote this number by Rn and try to get a recurrent formula for Rn: what is the relation betwen Rn and Rn−1? 2. The following problem is due to Leonardo of Pisa, later known as Fibonacci; it was introduced in his 1202 book Liber Abaci. Suppose a newly-born pair of rabbits, one male, one female, are put in a field. Rabbits are able to mate at the age of one month so that at the end of its second month a female can produce another pair of rabbits. Suppose that our rabbits never die and that the female always produces one new pair (one male, one female) every month from the second month on. How many pairs will there be in one year? 3. Daniel is coming up the staircase of 20 steps. He can either go one step at a time, or skip a step, moving two steps at a time. -
The Original Euler's Calculus-Of-Variations Method: Key
Submitted to EJP 1 Jozef Hanc, [email protected] The original Euler’s calculus-of-variations method: Key to Lagrangian mechanics for beginners Jozef Hanca) Technical University, Vysokoskolska 4, 042 00 Kosice, Slovakia Leonhard Euler's original version of the calculus of variations (1744) used elementary mathematics and was intuitive, geometric, and easily visualized. In 1755 Euler (1707-1783) abandoned his version and adopted instead the more rigorous and formal algebraic method of Lagrange. Lagrange’s elegant technique of variations not only bypassed the need for Euler’s intuitive use of a limit-taking process leading to the Euler-Lagrange equation but also eliminated Euler’s geometrical insight. More recently Euler's method has been resurrected, shown to be rigorous, and applied as one of the direct variational methods important in analysis and in computer solutions of physical processes. In our classrooms, however, the study of advanced mechanics is still dominated by Lagrange's analytic method, which students often apply uncritically using "variational recipes" because they have difficulty understanding it intuitively. The present paper describes an adaptation of Euler's method that restores intuition and geometric visualization. This adaptation can be used as an introductory variational treatment in almost all of undergraduate physics and is especially powerful in modern physics. Finally, we present Euler's method as a natural introduction to computer-executed numerical analysis of boundary value problems and the finite element method. I. INTRODUCTION In his pioneering 1744 work The method of finding plane curves that show some property of maximum and minimum,1 Leonhard Euler introduced a general mathematical procedure or method for the systematic investigation of variational problems. -
Upwind Summation by Parts Finite Difference Methods for Large Scale
Upwind summation by parts finite difference methods for large scale elastic wave simulations in 3D complex geometries ? Kenneth Durua,∗, Frederick Funga, Christopher Williamsa aMathematical Sciences Institute, Australian National University, Australia. Abstract High-order accurate summation-by-parts (SBP) finite difference (FD) methods constitute efficient numerical methods for simulating large-scale hyperbolic wave propagation problems. Traditional SBP FD operators that approximate first-order spatial derivatives with central-difference stencils often have spurious unresolved numerical wave-modes in their computed solutions. Recently derived high order accurate upwind SBP operators based non-central (upwind) FD stencils have the potential to suppress these poisonous spurious wave-modes on marginally resolved computational grids. In this paper, we demonstrate that not all high order upwind SBP FD operators are applicable. Numerical dispersion relation analysis shows that odd-order upwind SBP FD operators also support spurious unresolved high-frequencies on marginally resolved meshes. Meanwhile, even-order upwind SBP FD operators (of order 2; 4; 6) do not support spurious unresolved high frequency wave modes and also have better numerical dispersion properties. For all the upwind SBP FD operators we discretise the three space dimensional (3D) elastic wave equation on boundary-conforming curvilinear meshes. Using the energy method we prove that the semi-discrete ap- proximation is stable and energy-conserving. We derive a priori error estimate and prove the convergence of the numerical error. Numerical experiments for the 3D elastic wave equation in complex geometries corrobo- rate the theoretical analysis. Numerical simulations of the 3D elastic wave equation in heterogeneous media with complex non-planar free surface topography are given, including numerical simulations of community developed seismological benchmark problems. -
High Order Gradient, Curl and Divergence Conforming Spaces, with an Application to NURBS-Based Isogeometric Analysis
High order gradient, curl and divergence conforming spaces, with an application to compatible NURBS-based IsoGeometric Analysis R.R. Hiemstraa, R.H.M. Huijsmansa, M.I.Gerritsmab aDepartment of Marine Technology, Mekelweg 2, 2628CD Delft bDepartment of Aerospace Technology, Kluyverweg 2, 2629HT Delft Abstract Conservation laws, in for example, electromagnetism, solid and fluid mechanics, allow an exact discrete representation in terms of line, surface and volume integrals. We develop high order interpolants, from any basis that is a partition of unity, that satisfy these integral relations exactly, at cell level. The resulting gradient, curl and divergence conforming spaces have the propertythat the conservationlaws become completely independent of the basis functions. This means that the conservation laws are exactly satisfied even on curved meshes. As an example, we develop high ordergradient, curl and divergence conforming spaces from NURBS - non uniform rational B-splines - and thereby generalize the compatible spaces of B-splines developed in [1]. We give several examples of 2D Stokes flow calculations which result, amongst others, in a point wise divergence free velocity field. Keywords: Compatible numerical methods, Mixed methods, NURBS, IsoGeometric Analyis Be careful of the naive view that a physical law is a mathematical relation between previously defined quantities. The situation is, rather, that a certain mathematical structure represents a given physical structure. Burke [2] 1. Introduction In deriving mathematical models for physical theories, we frequently start with analysis on finite dimensional geometric objects, like a control volume and its bounding surfaces. We assign global, ’measurable’, quantities to these different geometric objects and set up balance statements. -
Arithmetic and Geometric Progressions
Arithmetic and geometric progressions mcTY-apgp-2009-1 This unit introduces sequences and series, and gives some simple examples of each. It also explores particular types of sequence known as arithmetic progressions (APs) and geometric progressions (GPs), and the corresponding series. In order to master the techniques explained here it is vital that you undertake plenty of practice exercises so that they become second nature. After reading this text, and/or viewing the video tutorial on this topic, you should be able to: • recognise the difference between a sequence and a series; • recognise an arithmetic progression; • find the n-th term of an arithmetic progression; • find the sum of an arithmetic series; • recognise a geometric progression; • find the n-th term of a geometric progression; • find the sum of a geometric series; • find the sum to infinity of a geometric series with common ratio r < 1. | | Contents 1. Sequences 2 2. Series 3 3. Arithmetic progressions 4 4. The sum of an arithmetic series 5 5. Geometric progressions 8 6. The sum of a geometric series 9 7. Convergence of geometric series 12 www.mathcentre.ac.uk 1 c mathcentre 2009 1. Sequences What is a sequence? It is a set of numbers which are written in some particular order. For example, take the numbers 1, 3, 5, 7, 9, .... Here, we seem to have a rule. We have a sequence of odd numbers. To put this another way, we start with the number 1, which is an odd number, and then each successive number is obtained by adding 2 to give the next odd number. -
Sequences, Series and Taylor Approximation (Ma2712b, MA2730)
Sequences, Series and Taylor Approximation (MA2712b, MA2730) Level 2 Teaching Team Current curator: Simon Shaw November 20, 2015 Contents 0 Introduction, Overview 6 1 Taylor Polynomials 10 1.1 Lecture 1: Taylor Polynomials, Definition . .. 10 1.1.1 Reminder from Level 1 about Differentiable Functions . .. 11 1.1.2 Definition of Taylor Polynomials . 11 1.2 Lectures 2 and 3: Taylor Polynomials, Examples . ... 13 x 1.2.1 Example: Compute and plot Tnf for f(x) = e ............ 13 1.2.2 Example: Find the Maclaurin polynomials of f(x) = sin x ...... 14 2 1.2.3 Find the Maclaurin polynomial T11f for f(x) = sin(x ) ....... 15 1.2.4 QuestionsforChapter6: ErrorEstimates . 15 1.3 Lecture 4 and 5: Calculus of Taylor Polynomials . .. 17 1.3.1 GeneralResults............................... 17 1.4 Lecture 6: Various Applications of Taylor Polynomials . ... 22 1.4.1 RelativeExtrema .............................. 22 1.4.2 Limits .................................... 24 1.4.3 How to Calculate Complicated Taylor Polynomials? . 26 1.5 ExerciseSheet1................................... 29 1.5.1 ExerciseSheet1a .............................. 29 1.5.2 FeedbackforSheet1a ........................... 33 2 Real Sequences 40 2.1 Lecture 7: Definitions, Limit of a Sequence . ... 40 2.1.1 DefinitionofaSequence .......................... 40 2.1.2 LimitofaSequence............................. 41 2.1.3 Graphic Representations of Sequences . .. 43 2.2 Lecture 8: Algebra of Limits, Special Sequences . ..... 44 2.2.1 InfiniteLimits................................ 44 1 2.2.2 AlgebraofLimits.............................. 44 2.2.3 Some Standard Convergent Sequences . .. 46 2.3 Lecture 9: Bounded and Monotone Sequences . ..... 48 2.3.1 BoundedSequences............................. 48 2.3.2 Convergent Sequences and Closed Bounded Intervals . .... 48 2.4 Lecture10:MonotoneSequences . -
The Pascal Matrix Function and Its Applications to Bernoulli Numbers and Bernoulli Polynomials and Euler Numbers and Euler Polynomials
The Pascal Matrix Function and Its Applications to Bernoulli Numbers and Bernoulli Polynomials and Euler Numbers and Euler Polynomials Tian-Xiao He ∗ Jeff H.-C. Liao y and Peter J.-S. Shiue z Dedicated to Professor L. C. Hsu on the occasion of his 95th birthday Abstract A Pascal matrix function is introduced by Call and Velleman in [3]. In this paper, we will use the function to give a unified approach in the study of Bernoulli numbers and Bernoulli poly- nomials. Many well-known and new properties of the Bernoulli numbers and polynomials can be established by using the Pascal matrix function. The approach is also applied to the study of Euler numbers and Euler polynomials. AMS Subject Classification: 05A15, 65B10, 33C45, 39A70, 41A80. Key Words and Phrases: Pascal matrix, Pascal matrix func- tion, Bernoulli number, Bernoulli polynomial, Euler number, Euler polynomial. ∗Department of Mathematics, Illinois Wesleyan University, Bloomington, Illinois 61702 yInstitute of Mathematics, Academia Sinica, Taipei, Taiwan zDepartment of Mathematical Sciences, University of Nevada, Las Vegas, Las Vegas, Nevada, 89154-4020. This work is partially supported by the Tzu (?) Tze foundation, Taipei, Taiwan, and the Institute of Mathematics, Academia Sinica, Taipei, Taiwan. The author would also thank to the Institute of Mathematics, Academia Sinica for the hospitality. 1 2 T. X. He, J. H.-C. Liao, and P. J.-S. Shiue 1 Introduction A large literature scatters widely in books and journals on Bernoulli numbers Bn, and Bernoulli polynomials Bn(x). They can be studied by means of the binomial expression connecting them, n X n B (x) = B xn−k; n ≥ 0: (1) n k k k=0 The study brings consistent attention of researchers working in combi- natorics, number theory, etc. -
Numerical Stability & Numerical Smoothness of Ordinary Differential
NUMERICAL STABILITY & NUMERICAL SMOOTHNESS OF ORDINARY DIFFERENTIAL EQUATIONS Kaitlin Reddinger A Thesis Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of MASTER OF ARTS August 2015 Committee: Tong Sun, Advisor So-Hsiang Chou Kimberly Rogers ii ABSTRACT Tong Sun, Advisor Although numerically stable algorithms can be traced back to the Babylonian period, it is be- lieved that the study of numerical methods for ordinary differential equations was not rigorously developed until the 1700s. Since then the field has expanded - first with Leonhard Euler’s method and then with the works of Augustin Cauchy, Carl Runge and Germund Dahlquist. Now applica- tions involving numerical methods can be found in a myriad of subjects. With several centuries worth of diligent study devoted to the crafting of well-conditioned problems, it is surprising that one issue in particular - numerical stability - continues to cloud the analysis and implementation of numerical approximation. According to professor Paul Glendinning from the University of Cambridge, “The stability of solutions of differential equations can be a very difficult property to pin down. Rigorous mathemat- ical definitions are often too prescriptive and it is not always clear which properties of solutions or equations are most important in the context of any particular problem. In practice, different definitions are used (or defined) according to the problem being considered. The effect of this confusion is that there are more than 57 varieties of stability to choose from” [10]. Although this quote is primarily in reference to nonlinear problems, it can most certainly be applied to the topic of numerical stability in general. -
Section 6-3 Arithmetic and Geometric Sequences
466 6 SEQUENCES, SERIES, AND PROBABILITY Section 6-3 Arithmetic and Geometric Sequences Arithmetic and Geometric Sequences nth-Term Formulas Sum Formulas for Finite Arithmetic Series Sum Formulas for Finite Geometric Series Sum Formula for Infinite Geometric Series For most sequences it is difficult to sum an arbitrary number of terms of the sequence without adding term by term. But particular types of sequences, arith- metic sequences and geometric sequences, have certain properties that lead to con- venient and useful formulas for the sums of the corresponding arithmetic series and geometric series. Arithmetic and Geometric Sequences The sequence 5, 7, 9, 11, 13,..., 5 ϩ 2(n Ϫ 1),..., where each term after the first is obtained by adding 2 to the preceding term, is an example of an arithmetic sequence. The sequence 5, 10, 20, 40, 80,..., 5 (2)nϪ1,..., where each term after the first is obtained by multiplying the preceding term by 2, is an example of a geometric sequence. ARITHMETIC SEQUENCE DEFINITION A sequence a , a , a ,..., a ,... 1 1 2 3 n is called an arithmetic sequence, or arithmetic progression, if there exists a constant d, called the common difference, such that Ϫ ϭ an anϪ1 d That is, ϭ ϩ Ͼ an anϪ1 d for every n 1 GEOMETRIC SEQUENCE DEFINITION A sequence a , a , a ,..., a ,... 2 1 2 3 n is called a geometric sequence, or geometric progression, if there exists a nonzero constant r, called the common ratio, such that 6-3 Arithmetic and Geometric Sequences 467 a n ϭ r DEFINITION anϪ1 2 That is, continued ϭ Ͼ an ranϪ1 for every n 1 Explore/Discuss (A) Graph the arithmetic sequence 5, 7, 9, ... -
Finite Difference Method for the Hull–White Partial Differential Equations
mathematics Review Finite Difference Method for the Hull–White Partial Differential Equations Yongwoong Lee 1 and Kisung Yang 2,* 1 Department of International Finance, College of Economics and Business, Hankuk University of Foreign Studies, 81 Oedae-ro, Mohyeon-eup, Cheoin-gu, Yongin-si 17035, Gyeonggi-do, Korea; [email protected] 2 School of Finance, College of Business Administration, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Korea * Correspondence: [email protected] Received: 3 August 2020; Accepted: 29 September 2020; Published: 7 October 2020 Abstract: This paper reviews the finite difference method (FDM) for pricing interest rate derivatives (IRDs) under the Hull–White Extended Vasicek model (HW model) and provides the MATLAB codes for it. Among the financial derivatives on various underlying assets, IRDs have the largest trading volume and the HW model is widely used for pricing them. We introduce general backgrounds of the HW model, its associated partial differential equations (PDEs), and FDM formulation for one- and two-asset problems. The two-asset problem is solved by the basic operator splitting method. For numerical tests, one- and two-asset bond options are considered. The computational results show close values to analytic solutions. We conclude with a brief comment on the research topics for the PDE approach to IRD pricing. Keywords: finite difference method; Hull–White model; operator splitting method 1. Introduction The finite difference method (FDM) has been widely used to compute the prices of financial derivatives numerically (Duffy [1]). Many studies on option pricing employ FDM, especially on models such as Black–Scholes partial differential equation (PDE) for equity or exchange rate derivatives (Kim et al.