Vol 45 Ams / Maa Anneli Lax New Mathematical Library
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Projects Applying Linear Algebra to Calculus
PROJECTS APPLYING LINEAR ALGEBRA TO CALCULUS Dr. Jason J. Molitierno Department of Mathematics Sacred Heart University Fairfield, CT 06825-1000 MAJOR PROBLEMS IN TEACHING A LINEAR ALGEBRA CLASS: • Having enough time in the semester to cover the syllabus. • Some problems are long and involved. ◦ Solving Systems of Equations. ◦ Finding Eigenvalues and Eigenvectors • Concepts can be difficult to visualize. ◦ Rotation and Translation of Vectors in multi-dimensioanl space. ◦ Solutions to systems of equations and systems of differential equations. • Some problems may be repetitive. A SOLUTION TO THESE ISSUES: OUT OF CLASS PROJECTS. THE BENEFITS: • By having the students learn some material outside of class, you have more time in class to cover additional material that you may not get to otherwise. • Students tend to get lost when the professor does a long computation on the board. Students are like "scribes." • Using technology, students can more easily visualize concepts. • Doing the more repetitive problems outside of class cuts down on boredom inside of class. • Gives students another method of learning as an alternative to be lectured to. APPLICATIONS OF SYSTEMS OF LINEAR EQUATIONS: (taken from Lay, fifth edition) • Nutrition: • Electrical Currents: • Population Distribution: VISUALIZING LINEAR TRANSFORMATIONS: in each of the five exercises below, do the following: (a) Find the matrix A that performs the desired transformation. (Note: In Exercises 4 and 5 where there are two transformations, multiply the two matrices with the first transformation matrix being on the right.) (b) Let T : <2 ! <2 be the transformation T (x) = Ax where A is the matrix you found in part (a). Find T (2; 3). -
Direct Shear Mapping – a New Weak Lensing Tool
MNRAS 451, 2161–2173 (2015) doi:10.1093/mnras/stv1083 Direct shear mapping – a new weak lensing tool C. O. de Burgh-Day,1,2,3‹ E. N. Taylor,1,3‹ R. L. Webster1,3‹ and A. M. Hopkins2,3 1School of Physics, David Caro Building, The University of Melbourne, Parkville VIC 3010, Australia 2The Australian Astronomical Observatory, PO Box 915, North Ryde NSW 1670, Australia 3ARC Centre of Excellence for All-sky Astrophysics (CAASTRO), Australian National University, Canberra, ACT 2611, Australia Accepted 2015 May 12. Received 2015 February 16; in original form 2013 October 24 ABSTRACT We have developed a new technique called direct shear mapping (DSM) to measure gravita- tional lensing shear directly from observations of a single background source. The technique assumes the velocity map of an unlensed, stably rotating galaxy will be rotationally symmet- ric. Lensing distorts the velocity map making it asymmetric. The degree of lensing can be inferred by determining the transformation required to restore axisymmetry. This technique is in contrast to traditional weak lensing methods, which require averaging an ensemble of background galaxy ellipticity measurements, to obtain a single shear measurement. We have tested the efficacy of our fitting algorithm with a suite of systematic tests on simulated data. We demonstrate that we are in principle able to measure shears as small as 0.01. In practice, we have fitted for the shear in very low redshift (and hence unlensed) velocity maps, and have obtained null result with an error of ±0.01. This high-sensitivity results from analysing spa- tially resolved spectroscopic images (i.e. -
Taxicab Space, Inversion, Harmonic Conjugates, Taxicab Sphere
TAXICAB SPHERICAL INVERSIONS IN TAXICAB SPACE ADNAN PEKZORLU∗, AYS¸E BAYAR DEPARTMENT OF MATHEMATICS-COMPUTER UNIVERSITY OF ESKIS¸EHIR OSMANGAZI E-MAILS: [email protected], [email protected] (Received: 11 June 2019, Accepted: 29 May 2020) Abstract. In this paper, we define an inversion with respect to a taxicab sphere in the three dimensional taxicab space and prove several properties of this inversion. We also study cross ratio, harmonic conjugates and the inverse images of lines, planes and taxicab spheres in three dimensional taxicab space. AMS Classification: 40Exx, 51Fxx, 51B20, 51K99. Keywords: taxicab space, inversion, harmonic conjugates, taxicab sphere. 1. Introduction The inversion was introduced by Apollonius of Perga in his last book Plane Loci, and systematically studied and applied by Steiner about 1820s, [2]. During the following decades, many physicists and mathematicians independently rediscovered inversions, proving the properties that were most useful for their particular applica- tions by defining a central cone, ellipse and circle inversion. Some of these features are inversion compared to the classical circle. Inversion transformation and basic concepts have been presented in literature. The inversions with respect to the central conics in real Euclidean plane was in- troduced in [3]. Then the inversions with respect to ellipse was studied detailed in ∗ CORRESPONDING AUTHOR JOURNAL OF MAHANI MATHEMATICAL RESEARCH CENTER VOL. 9, NUMBERS 1-2 (2020) 45-54. DOI: 10.22103/JMMRC.2020.14232.1095 c MAHANI MATHEMATICAL RESEARCH CENTER 45 46 ADNAN PEKZORLU, AYS¸E BAYAR [13]. In three-dimensional space a generalization of the spherical inversion is given in [16]. Also, the inversions with respect to the taxicab distance, α-distance [18], [4], or in general a p-distance [11]. -
Multi-View Metric Learning in Vector-Valued Kernel Spaces
Multi-view Metric Learning in Vector-valued Kernel Spaces Riikka Huusari Hachem Kadri Cécile Capponi Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France Abstract unlabeled examples [26]; the latter tries to efficiently combine multiple kernels defined on each view to exploit We consider the problem of metric learning for information coming from different representations [11]. multi-view data and present a novel method More recently, vector-valued reproducing kernel Hilbert for learning within-view as well as between- spaces (RKHSs) have been introduced to the field of view metrics in vector-valued kernel spaces, multi-view learning for going further than MKL by in- as a way to capture multi-modal structure corporating in the learning model both within-view and of the data. We formulate two convex opti- between-view dependencies [20, 14]. It turns out that mization problems to jointly learn the metric these kernels and their associated vector-valued repro- and the classifier or regressor in kernel feature ducing Hilbert spaces provide a unifying framework for spaces. An iterative three-step multi-view a number of previous multi-view kernel methods, such metric learning algorithm is derived from the as co-regularized multi-view learning and manifold reg- optimization problems. In order to scale the ularization, and naturally allow to encode within-view computation to large training sets, a block- as well as between-view similarities [21]. wise Nyström approximation of the multi-view Kernels of vector-valued RKHSs are positive semidefi- kernel matrix is introduced. We justify our ap- nite matrix-valued functions. They have been applied proach theoretically and experimentally, and with success in various machine learning problems, such show its performance on real-world datasets as multi-task learning [10], functional regression [15] against relevant state-of-the-art methods. -
Taxicab Geometry, Or When a Circle Is a Square
Taxicab Geometry, or When a Circle is a Square Circle on the Road 2012, Math Festival Olga Radko (Los Angeles Math Circle, UCLA) April 14th, 2012 Abstract The distance between two points is the length of the shortest path connecting them. In plane Euclidean geometry such a path is along the straight line connecting the two points. In contrast, in a city consisting of a square grid of streets shortest paths between two points are no longer straight lines (as every cab driver knows). We will explore the geometry of this unusual distance and play several related games. 1 René Descartes (1596-1650) was a French mathematician, philosopher and writer. Among his many accomplishments, he developed a very convenient way to describe positions of points on a plane. This method was very important for fu- ture development of mathematics and physics. We will start learning about this invention today. The city of Descartes is a plane that extends infinitely in all directions: The center of the city is marked by point O. • The horizontal (West-East) line going through O is called • the x-axis. The vertical (South-North) line going through O is called • the y-axis. Each house in the city is represented by a point which • is the intersection of a vertical and a horizontal line. Each house has an address which consists of two whole numbers written inside of parenthesis. 2 Example. Point A shown below has coordinates (2, 3). The first number tells you the distance to the y-axis. • The distance is positive if you are on the right of the y-axis. -
Geometry of Some Taxicab Curves
GEOMETRY OF SOME TAXICAB CURVES Maja Petrović 1 Branko Malešević 2 Bojan Banjac 3 Ratko Obradović 4 Abstract In this paper we present geometry of some curves in Taxicab metric. All curves of second order and trifocal ellipse in this metric are presented. Area and perimeter of some curves are also defined. Key words: Taxicab metric, Conics, Trifocal ellipse 1. INTRODUCTION In this paper, taxicab and standard Euclidean metrics for a visual representation of some planar curves are considered. Besides the term taxicab, also used are Manhattan, rectangular metric and city block distance [4], [5], [7]. This metric is a special case of the Minkowski 1 MSc Maja Petrović, assistant at Faculty of Transport and Traffic Engineering, University of Belgrade, Serbia, e-mail: [email protected] 2 PhD Branko Malešević, associate professor at Faculty of Electrical Engineering, Department of Applied Mathematics, University of Belgrade, Serbia, e-mail: [email protected] 3 MSc Bojan Banjac, student of doctoral studies of Software Engineering, Faculty of Electrical Engineering, University of Belgrade, Serbia, assistant at Faculty of Technical Sciences, Computer Graphics Chair, University of Novi Sad, Serbia, e-mail: [email protected] 4 PhD Ratko Obradović, full professor at Faculty of Technical Sciences, Computer Graphics Chair, University of Novi Sad, Serbia, e-mail: [email protected] 1 metrics of order 1, which is for distance between two points , and , determined by: , | | | | (1) Minkowski metrics contains taxicab metric for value 1 and Euclidean metric for 2. The term „taxicab geometry“ was first used by K. Menger in the book [9]. -
LAB 9.1 Taxicab Versus Euclidean Distance
([email protected] LAB 9.1 Name(s) Taxicab Versus Euclidean Distance Equipment: Geoboard, graph or dot paper If you can travel only horizontally or vertically (like a taxicab in a city where all streets run North-South and East-West), the distance you have to travel to get from the origin to the point (2, 3) is 5.This is called the taxicab distance between (0, 0) and (2, 3). If, on the other hand, you can go from the origin to (2, 3) in a straight line, the distance you travel is called the Euclidean distance, or just the distance. Finding taxicab distance: Taxicab distance can be measured between any two points, whether on a street or not. For example, the taxicab distance from (1.2, 3.4) to (9.9, 9.9) is the sum of 8.7 (the horizontal component) and 6.5 (the vertical component), for a total of 15.2. 1. What is the taxicab distance from (2, 3) to the following points? a. (7, 9) b. (–3, 8) c. (2, –1) d. (6, 5.4) e. (–1.24, 3) f. (–1.24, 5.4) Finding Euclidean distance: There are various ways to calculate Euclidean distance. Here is one method that is based on the sides and areas of squares. Since the area of the square at right is y 13 (why?), the side of the square—and therefore the Euclidean distance from, say, the origin to the point (2,3)—must be ͙ෆ13,or approximately 3.606 units. 3 x 2 Geometry Labs Section 9 Distance and Square Root 121 © 1999 Henri Picciotto, www.MathEducationPage.org ([email protected] LAB 9.1 Name(s) Taxicab Versus Euclidean Distance (continued) 2. -
MATRICES Part 2 3. Linear Equations
MATRICES part 2 (modified content from Wikipedia articles on matrices http://en.wikipedia.org/wiki/Matrix_(mathematics)) 3. Linear equations Matrices can be used to compactly write and work with systems of linear equations. For example, if A is an m-by-n matrix, x designates a column vector (i.e., n×1-matrix) of n variables x1, x2, ..., xn, and b is an m×1-column vector, then the matrix equation Ax = b is equivalent to the system of linear equations a1,1x1 + a1,2x2 + ... + a1,nxn = b1 ... am,1x1 + am,2x2 + ... + am,nxn = bm . For example, 3x1 + 2x2 – x3 = 1 2x1 – 2x2 + 4x3 = – 2 – x1 + 1/2x2 – x3 = 0 is a system of three equations in the three variables x1, x2, and x3. This can be written in the matrix form Ax = b where 3 2 1 1 1 A = 2 2 4 , x = 2 , b = 2 1 1/2 −1 푥3 0 � − � �푥 � �− � A solution to− a linear system− is an assignment푥 of numbers to the variables such that all the equations are simultaneously satisfied. A solution to the system above is given by x1 = 1 x2 = – 2 x3 = – 2 since it makes all three equations valid. A linear system may behave in any one of three possible ways: 1. The system has infinitely many solutions. 2. The system has a single unique solution. 3. The system has no solution. 3.1. Solving linear equations There are several algorithms for solving a system of linear equations. Elimination of variables The simplest method for solving a system of linear equations is to repeatedly eliminate variables. -
Karhunen-Loève Analysis for Weak Gravitational Lensing
c Copyright 2012 Jacob T. Vanderplas arXiv:1301.6657v1 [astro-ph.CO] 28 Jan 2013 Karhunen-Lo`eve Analysis for Weak Gravitational Lensing Jacob T. Vanderplas A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2012 Reading Committee: Andrew Connolly, Chair Bhuvnesh Jain Andrew Becker Program Authorized to Offer Degree: Department of Astronomy University of Washington Abstract Karhunen-Lo`eve Analysis for Weak Gravitational Lensing Jacob T. Vanderplas Chair of the Supervisory Committee: Professor Andrew Connolly Department of Astronomy In the past decade, weak gravitational lensing has become an important tool in the study of the universe at the largest scale, giving insights into the distribution of dark matter, the expansion of the universe, and the nature of dark energy. This thesis research explores several applications of Karhunen-Lo`eve (KL) analysis to speed and improve the comparison of weak lensing shear catalogs to theory in order to constrain cosmological parameters in current and future lensing surveys. This work addresses three related aspects of weak lensing analysis: Three-dimensional Tomographic Mapping: (Based on work published in VanderPlas et al., 2011) We explore a new fast approach to three-dimensional mass mapping in weak lensing surveys. The KL approach uses a KL-based filtering of the shear signal to reconstruct mass structures on the line-of-sight, and provides a unified framework to evaluate the efficacy of linear reconstruction techniques. We find that the KL-based filtering leads to near-optimal angular resolution, and computation times which are faster than previous approaches. -
From Circle to Hyperbola in Taxicab Geometry
Abstract: The purpose of this article is to provide insight into the shape of circles and related geometric figures in taxicab geometry. This will enable teachers to guide student explorations by designing interesting worksheets, leading knowledgeable class room discussions, and be aware of the different results that students can obtain if they decide to delve deeper into this fascinating subject. From Circle to Hyperbola in Taxicab Geometry Ruth I. Berger, Luther College How far is the shortest path from Grand Central Station to the Empire State building? A pigeon could fly there in a straight line, but a person is confined by the street grid. The geometry obtained from measuring the distance between points by the actual distance traveled on a square grid is known as taxicab geometry. This topic can engage students at all levels, from plotting points and observing surprising shapes, to examining the underlying reasons for why these figures take on this appearance. Having to work with a new distance measurement takes everyone out of their comfort zone of routine memorization and makes them think, even about definitions and facts that seemed obvious before. It can also generate lively group discussions. There are many good resources on taxicab geometry. The ideas from Krause’s classic book [Krause 1986] have been picked up in recent NCTM publications [Dreiling 2012] and [Smith 2013], the latter includes an extensive pedagogy discussion. [House 2005] provides nice worksheets. Definition: Let A and B be points with coordinates (푥1, 푦1) and (푥2, 푦2), respectively. The taxicab distance from A to B is defined as 푑푖푠푡(퐴, 퐵) = |푥1 − 푥2| + |푦1 − 푦2|. -
Math 105 Workbook Exploring Mathematics
Math 105 Workbook Exploring Mathematics Douglas R. Anderson, Professor Fall 2018: MWF 11:50-1:00, ISC 101 Acknowledgment First we would like to thank all of our former Math 105 students. Their successes, struggles, and suggestions have shaped how we teach this course in many important ways. We also want to thank our departmental colleagues and several Concordia math- ematics majors for many fruitful discussions and resources on the content of this course and the makeup of this workbook. Some of the topics, examples, and exercises in this workbook are drawn from other works. Most significantly, we thank Samantha Briggs, Ellen Kramer, and Dr. Jessie Lenarz for their work in Exploring Mathematics, as well as other Cobber mathemat- ics professors. We have also used: • Taxicab Geometry: An Adventure in Non-Euclidean Geometry by Eugene F. Krause, • Excursions in Modern Mathematics, Sixth Edition, by Peter Tannenbaum. • Introductory Graph Theory by Gary Chartrand, • The Heart of Mathematics: An invitation to effective thinking by Edward B. Burger and Michael Starbird, • Applied Finite Mathematics by Edmond C. Tomastik. Finally, we want to thank (in advance) you, our current students. Your suggestions for this course and this workbook are always encouraged, either in person or over e-mail. Both the course and workbook are works in progress that will continue to improve each semester with your help. Let's have a great semester this fall exploring mathematics together and fulfilling Concordia's math requirement in 2018. Skol Cobbs! i ii Contents 1 Taxicab Geometry 3 1.1 Taxicab Distance . .3 Homework . .8 1.2 Taxicab Circles . -
Taxicab Geometry
TAXICAB GEOMETRY MICHAEL A. HALL 11/13/2011 EUCLIDEAN GEOMETRY In geometry the primary objects of study are points, lines, angles, and distances. We can identify each point in the plane by its Cartesian coordinates (x; y). The Euclidean distance between points A = (x1; y1) and B = (x2; y2) is p 2 2 dE(A; B) = (x2 − x1) + (y2 − y1) : (Euclidean distance formula) TAXICAB GEOMETRY In “taxicab” geometry, the points, lines, and angles are the same, but the notion of distance is different from the Euclidean distance. The taxicab distance between points A = (x1; y1) and B = (x2; y2) is dT (A; B) = jx2 − x1j + jy2 − y1j: (Taxicab distance formula) Exercises. (1) On a sheet of graph paper, mark each pair of points P and Q and find the both the Euclidean and taxicab distance between them: (a) P = (0; 0), Q = (1; 1) (b) P = (1; 2), Q = (2; 3) (c) P = (1; 0), Q = (5; 0) (2) (Taxi circles) Let A be the point with coordinates (2; 2). (a) Plot A on a piece of graph paper, and mark all points P such that dT (A; P ) = 1. The set of such points is written fP j dT (A; P ) = 1g. Also mark all points in fP j dT (A; P ) = 2g. (b) Graph the set of points which are a distance 2 from the point B = (1; 1). (3) (Lines) Recall that in taxicab geometry, the shapes we call lines are the same as the usual lines in Euclidean geometry. Graph the line ` that passes through the points (3; 0) and (0; 3).