Brief Review of Complex Numbers 1 Representations
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Fortran 90 Overview
1 Fortran 90 Overview J.E. Akin, Copyright 1998 This overview of Fortran 90 (F90) features is presented as a series of tables that illustrate the syntax and abilities of F90. Frequently comparisons are made to similar features in the C++ and F77 languages and to the Matlab environment. These tables show that F90 has significant improvements over F77 and matches or exceeds newer software capabilities found in C++ and Matlab for dynamic memory management, user defined data structures, matrix operations, operator definition and overloading, intrinsics for vector and parallel pro- cessors and the basic requirements for object-oriented programming. They are intended to serve as a condensed quick reference guide for programming in F90 and for understanding programs developed by others. List of Tables 1 Comment syntax . 4 2 Intrinsic data types of variables . 4 3 Arithmetic operators . 4 4 Relational operators (arithmetic and logical) . 5 5 Precedence pecking order . 5 6 Colon Operator Syntax and its Applications . 5 7 Mathematical functions . 6 8 Flow Control Statements . 7 9 Basic loop constructs . 7 10 IF Constructs . 8 11 Nested IF Constructs . 8 12 Logical IF-ELSE Constructs . 8 13 Logical IF-ELSE-IF Constructs . 8 14 Case Selection Constructs . 9 15 F90 Optional Logic Block Names . 9 16 GO TO Break-out of Nested Loops . 9 17 Skip a Single Loop Cycle . 10 18 Abort a Single Loop . 10 19 F90 DOs Named for Control . 10 20 Looping While a Condition is True . 11 21 Function definitions . 11 22 Arguments and return values of subprograms . 12 23 Defining and referring to global variables . -
Quick Overview: Complex Numbers
Quick Overview: Complex Numbers February 23, 2012 1 Initial Definitions Definition 1 The complex number z is defined as: z = a + bi (1) p where a, b are real numbers and i = −1. Remarks about the definition: • Engineers typically use j instead of i. • Examples of complex numbers: p 5 + 2i; 3 − 2i; 3; −5i • Powers of i: i2 = −1 i3 = −i i4 = 1 i5 = i i6 = −1 i7 = −i . • All real numbers are also complex (by taking b = 0). 2 Visualizing Complex Numbers A complex number is defined by it's two real numbers. If we have z = a + bi, then: Definition 2 The real part of a + bi is a, Re(z) = Re(a + bi) = a The imaginary part of a + bi is b, Im(z) = Im(a + bi) = b 1 Im(z) 4i 3i z = a + bi 2i r b 1i θ Re(z) a −1i Figure 1: Visualizing z = a + bi in the complex plane. Shown are the modulus (or length) r and the argument (or angle) θ. To visualize a complex number, we use the complex plane C, where the horizontal (or x-) axis is for the real part, and the vertical axis is for the imaginary part. That is, a + bi is plotted as the point (a; b). In Figure 1, we can see that it is also possible to represent the point a + bi, or (a; b) in polar form, by computing its modulus (or size), and angle (or argument): p r = jzj = a2 + b2 θ = arg(z) We have to be a bit careful defining φ, since there are many ways to write φ (and we could add multiples of 2π as well). -
Niobrara County School District #1 Curriculum Guide
NIOBRARA COUNTY SCHOOL DISTRICT #1 CURRICULUM GUIDE th SUBJECT: Math Algebra II TIMELINE: 4 quarter Domain: Student Friendly Level of Resource Academic Standard: Learning Objective Thinking Correlation/Exemplar Vocabulary [Assessment] [Mathematical Practices] Domain: Perform operations on matrices and use matrices in applications. Standards: N-VM.6.Use matrices to I can represent and Application Pearson Alg. II Textbook: Matrix represent and manipulated data, e.g., manipulate data to represent --Lesson 12-2 p. 772 (Matrices) to represent payoffs or incidence data. M --Concept Byte 12-2 p. relationships in a network. 780 Data --Lesson 12-5 p. 801 [Assessment]: --Concept Byte 12-2 p. 780 [Mathematical Practices]: Niobrara County School District #1, Fall 2012 Page 1 NIOBRARA COUNTY SCHOOL DISTRICT #1 CURRICULUM GUIDE th SUBJECT: Math Algebra II TIMELINE: 4 quarter Domain: Student Friendly Level of Resource Academic Standard: Learning Objective Thinking Correlation/Exemplar Vocabulary [Assessment] [Mathematical Practices] Domain: Perform operations on matrices and use matrices in applications. Standards: N-VM.7. Multiply matrices I can multiply matrices by Application Pearson Alg. II Textbook: Scalars by scalars to produce new matrices, scalars to produce new --Lesson 12-2 p. 772 e.g., as when all of the payoffs in a matrices. M --Lesson 12-5 p. 801 game are doubled. [Assessment]: [Mathematical Practices]: Domain: Perform operations on matrices and use matrices in applications. Standards:N-VM.8.Add, subtract and I can perform operations on Knowledge Pearson Alg. II Common Matrix multiply matrices of appropriate matrices and reiterate the Core Textbook: Dimensions dimensions. limitations on matrix --Lesson 12-1p. 764 [Assessment]: dimensions. -
Complex Eigenvalues
Quantitative Understanding in Biology Module III: Linear Difference Equations Lecture II: Complex Eigenvalues Introduction In the previous section, we considered the generalized two- variable system of linear difference equations, and showed that the eigenvalues of these systems are given by the equation… ± 2 4 = 1,2 2 √ − …where b = (a11 + a22), c=(a22 ∙ a11 – a12 ∙ a21), and ai,j are the elements of the 2 x 2 matrix that define the linear system. Inspecting this relation shows that it is possible for the eigenvalues of such a system to be complex numbers. Because our plants and seeds model was our first look at these systems, it was carefully constructed to avoid this complication [exercise: can you show that http://www.xkcd.org/179 this model cannot produce complex eigenvalues]. However, many systems of biological interest do have complex eigenvalues, so it is important that we understand how to deal with and interpret them. We’ll begin with a review of the basic algebra of complex numbers, and then consider their meaning as eigenvalues of dynamical systems. A Review of Complex Numbers You may recall that complex numbers can be represented with the notation a+bi, where a is the real part of the complex number, and b is the imaginary part. The symbol i denotes 1 (recall i2 = -1, i3 = -i and i4 = +1). Hence, complex numbers can be thought of as points on a complex plane, which has real √− and imaginary axes. In some disciplines, such as electrical engineering, you may see 1 represented by the symbol j. √− This geometric model of a complex number suggests an alternate, but equivalent, representation. -
Fortran Math Special Functions Library
IMSL® Fortran Math Special Functions Library Version 2021.0 Copyright 1970-2021 Rogue Wave Software, Inc., a Perforce company. Visual Numerics, IMSL, and PV-WAVE are registered trademarks of Rogue Wave Software, Inc., a Perforce company. IMPORTANT NOTICE: Information contained in this documentation is subject to change without notice. Use of this docu- ment is subject to the terms and conditions of a Rogue Wave Software License Agreement, including, without limitation, the Limited Warranty and Limitation of Liability. ACKNOWLEDGMENTS Use of the Documentation and implementation of any of its processes or techniques are the sole responsibility of the client, and Perforce Soft- ware, Inc., assumes no responsibility and will not be liable for any errors, omissions, damage, or loss that might result from any use or misuse of the Documentation PERFORCE SOFTWARE, INC. MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THE DOCUMENTATION. THE DOCU- MENTATION IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND. PERFORCE SOFTWARE, INC. HEREBY DISCLAIMS ALL WARRANTIES AND CONDITIONS WITH REGARD TO THE DOCUMENTATION, WHETHER EXPRESS, IMPLIED, STATUTORY, OR OTHERWISE, INCLUDING WITHOUT LIMITATION ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PAR- TICULAR PURPOSE, OR NONINFRINGEMENT. IN NO EVENT SHALL PERFORCE SOFTWARE, INC. BE LIABLE, WHETHER IN CONTRACT, TORT, OR OTHERWISE, FOR ANY SPECIAL, CONSEQUENTIAL, INDIRECT, PUNITIVE, OR EXEMPLARY DAMAGES IN CONNECTION WITH THE USE OF THE DOCUMENTATION. The Documentation is subject to change at any time without notice. IMSL https://www.imsl.com/ Contents Introduction The IMSL Fortran Numerical Libraries . 1 Getting Started . 2 Finding the Right Routine . 3 Organization of the Documentation . 4 Naming Conventions . -
Introduction Into Quaternions for Spacecraft Attitude Representation
Introduction into quaternions for spacecraft attitude representation Dipl. -Ing. Karsten Groÿekatthöfer, Dr. -Ing. Zizung Yoon Technical University of Berlin Department of Astronautics and Aeronautics Berlin, Germany May 31, 2012 Abstract The purpose of this paper is to provide a straight-forward and practical introduction to quaternion operation and calculation for rigid-body attitude representation. Therefore the basic quaternion denition as well as transformation rules and conversion rules to or from other attitude representation parameters are summarized. The quaternion computation rules are supported by practical examples to make each step comprehensible. 1 Introduction Quaternions are widely used as attitude represenation parameter of rigid bodies such as space- crafts. This is due to the fact that quaternion inherently come along with some advantages such as no singularity and computationally less intense compared to other attitude parameters such as Euler angles or a direction cosine matrix. Mainly, quaternions are used to • Parameterize a spacecraft's attitude with respect to reference coordinate system, • Propagate the attitude from one moment to the next by integrating the spacecraft equa- tions of motion, • Perform a coordinate transformation: e.g. calculate a vector in body xed frame from a (by measurement) known vector in inertial frame. However, dierent references use several notations and rules to represent and handle attitude in terms of quaternions, which might be confusing for newcomers [5], [4]. Therefore this article gives a straight-forward and clearly notated introduction into the subject of quaternions for attitude representation. The attitude of a spacecraft is its rotational orientation in space relative to a dened reference coordinate system. -
0.999… = 1 an Infinitesimal Explanation Bryan Dawson
0 1 2 0.9999999999999999 0.999… = 1 An Infinitesimal Explanation Bryan Dawson know the proofs, but I still don’t What exactly does that mean? Just as real num- believe it.” Those words were uttered bers have decimal expansions, with one digit for each to me by a very good undergraduate integer power of 10, so do hyperreal numbers. But the mathematics major regarding hyperreals contain “infinite integers,” so there are digits This fact is possibly the most-argued- representing not just (the 237th digit past “Iabout result of arithmetic, one that can evoke great the decimal point) and (the 12,598th digit), passion. But why? but also (the Yth digit past the decimal point), According to Robert Ely [2] (see also Tall and where is a negative infinite hyperreal integer. Vinner [4]), the answer for some students lies in their We have four 0s followed by a 1 in intuition about the infinitely small: While they may the fifth decimal place, and also where understand that the difference between and 1 is represents zeros, followed by a 1 in the Yth less than any positive real number, they still perceive a decimal place. (Since we’ll see later that not all infinite nonzero but infinitely small difference—an infinitesimal hyperreal integers are equal, a more precise, but also difference—between the two. And it’s not just uglier, notation would be students; most professional mathematicians have not or formally studied infinitesimals and their larger setting, the hyperreal numbers, and as a result sometimes Confused? Perhaps a little background information wonder . -
Measuring Fractals by Infinite and Infinitesimal Numbers
MEASURING FRACTALS BY INFINITE AND INFINITESIMAL NUMBERS Yaroslav D. Sergeyev DEIS, University of Calabria, Via P. Bucci, Cubo 42-C, 87036 Rende (CS), Italy, N.I. Lobachevsky State University, Nizhni Novgorod, Russia, and Institute of High Performance Computing and Networking of the National Research Council of Italy http://wwwinfo.deis.unical.it/∼yaro e-mail: [email protected] Abstract. Traditional mathematical tools used for analysis of fractals allow one to distinguish results of self-similarity processes after a finite number of iterations. For example, the result of procedure of construction of Cantor’s set after two steps is different from that obtained after three steps. However, we are not able to make such a distinction at infinity. It is shown in this paper that infinite and infinitesimal numbers proposed recently allow one to measure results of fractal processes at different iterations at infinity too. First, the new technique is used to measure at infinity sets being results of Cantor’s proce- dure. Second, it is applied to calculate the lengths of polygonal geometric spirals at different points of infinity. 1. INTRODUCTION During last decades fractals have been intensively studied and applied in various fields (see, for instance, [4, 11, 5, 7, 12, 20]). However, their mathematical analysis (except, of course, a very well developed theory of fractal dimensions) very often continues to have mainly a qualitative character and there are no many tools for a quantitative analysis of their behavior after execution of infinitely many steps of a self-similarity process of construction. Usually, we can measure fractals in a way and can give certain numerical answers to questions regarding fractals only if a finite number of steps in the procedure of their construction has been executed. -
COMPLEX EIGENVALUES Math 21B, O. Knill
COMPLEX EIGENVALUES Math 21b, O. Knill NOTATION. Complex numbers are written as z = x + iy = r exp(iφ) = r cos(φ) + ir sin(φ). The real number r = z is called the absolute value of z, the value φ isjthej argument and denoted by arg(z). Complex numbers contain the real numbers z = x+i0 as a subset. One writes Re(z) = x and Im(z) = y if z = x + iy. ARITHMETIC. Complex numbers are added like vectors: x + iy + u + iv = (x + u) + i(y + v) and multiplied as z w = (x + iy)(u + iv) = xu yv + i(yu xv). If z = 0, one can divide 1=z = 1=(x + iy) = (x iy)=(x2 + y2). ∗ − − 6 − ABSOLUTE VALUE AND ARGUMENT. The absolute value z = x2 + y2 satisfies zw = z w . The argument satisfies arg(zw) = arg(z) + arg(w). These are direct jconsequencesj of the polarj represenj j jtationj j z = p r exp(iφ); w = s exp(i ); zw = rs exp(i(φ + )). x GEOMETRIC INTERPRETATION. If z = x + iy is written as a vector , then multiplication with an y other complex number w is a dilation-rotation: a scaling by w and a rotation by arg(w). j j THE DE MOIVRE FORMULA. zn = exp(inφ) = cos(nφ) + i sin(nφ) = (cos(φ) + i sin(φ))n follows directly from z = exp(iφ) but it is magic: it leads for example to formulas like cos(3φ) = cos(φ)3 3 cos(φ) sin2(φ) which would be more difficult to come by using geometrical or power series arguments. -
IEEE Standard 754 for Binary Floating-Point Arithmetic
Work in Progress: Lecture Notes on the Status of IEEE 754 October 1, 1997 3:36 am Lecture Notes on the Status of IEEE Standard 754 for Binary Floating-Point Arithmetic Prof. W. Kahan Elect. Eng. & Computer Science University of California Berkeley CA 94720-1776 Introduction: Twenty years ago anarchy threatened floating-point arithmetic. Over a dozen commercially significant arithmetics boasted diverse wordsizes, precisions, rounding procedures and over/underflow behaviors, and more were in the works. “Portable” software intended to reconcile that numerical diversity had become unbearably costly to develop. Thirteen years ago, when IEEE 754 became official, major microprocessor manufacturers had already adopted it despite the challenge it posed to implementors. With unprecedented altruism, hardware designers had risen to its challenge in the belief that they would ease and encourage a vast burgeoning of numerical software. They did succeed to a considerable extent. Anyway, rounding anomalies that preoccupied all of us in the 1970s afflict only CRAY X-MPs — J90s now. Now atrophy threatens features of IEEE 754 caught in a vicious circle: Those features lack support in programming languages and compilers, so those features are mishandled and/or practically unusable, so those features are little known and less in demand, and so those features lack support in programming languages and compilers. To help break that circle, those features are discussed in these notes under the following headings: Representable Numbers, Normal and Subnormal, Infinite -
Calculus Terminology
AP Calculus BC Calculus Terminology Absolute Convergence Asymptote Continued Sum Absolute Maximum Average Rate of Change Continuous Function Absolute Minimum Average Value of a Function Continuously Differentiable Function Absolutely Convergent Axis of Rotation Converge Acceleration Boundary Value Problem Converge Absolutely Alternating Series Bounded Function Converge Conditionally Alternating Series Remainder Bounded Sequence Convergence Tests Alternating Series Test Bounds of Integration Convergent Sequence Analytic Methods Calculus Convergent Series Annulus Cartesian Form Critical Number Antiderivative of a Function Cavalieri’s Principle Critical Point Approximation by Differentials Center of Mass Formula Critical Value Arc Length of a Curve Centroid Curly d Area below a Curve Chain Rule Curve Area between Curves Comparison Test Curve Sketching Area of an Ellipse Concave Cusp Area of a Parabolic Segment Concave Down Cylindrical Shell Method Area under a Curve Concave Up Decreasing Function Area Using Parametric Equations Conditional Convergence Definite Integral Area Using Polar Coordinates Constant Term Definite Integral Rules Degenerate Divergent Series Function Operations Del Operator e Fundamental Theorem of Calculus Deleted Neighborhood Ellipsoid GLB Derivative End Behavior Global Maximum Derivative of a Power Series Essential Discontinuity Global Minimum Derivative Rules Explicit Differentiation Golden Spiral Difference Quotient Explicit Function Graphic Methods Differentiable Exponential Decay Greatest Lower Bound Differential -
Complex Numbers Sigma-Complex3-2009-1 in This Unit We Describe Formally What Is Meant by a Complex Number
Complex numbers sigma-complex3-2009-1 In this unit we describe formally what is meant by a complex number. First let us revisit the solution of a quadratic equation. Example Use the formula for solving a quadratic equation to solve x2 10x +29=0. − Solution Using the formula b √b2 4ac x = − ± − 2a with a =1, b = 10 and c = 29, we find − 10 p( 10)2 4(1)(29) x = ± − − 2 10 √100 116 x = ± − 2 10 √ 16 x = ± − 2 Now using i we can find the square root of 16 as 4i, and then write down the two solutions of the equation. − 10 4 i x = ± =5 2 i 2 ± The solutions are x = 5+2i and x =5-2i. Real and imaginary parts We have found that the solutions of the equation x2 10x +29=0 are x =5 2i. The solutions are known as complex numbers. A complex number− such as 5+2i is made up± of two parts, a real part 5, and an imaginary part 2. The imaginary part is the multiple of i. It is common practice to use the letter z to stand for a complex number and write z = a + b i where a is the real part and b is the imaginary part. Key Point If z is a complex number then we write z = a + b i where i = √ 1 − where a is the real part and b is the imaginary part. www.mathcentre.ac.uk 1 c mathcentre 2009 Example State the real and imaginary parts of 3+4i.