Six Mathematical Gems from the History of Distance Geometry
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Glossary Physics (I-Introduction)
1 Glossary Physics (I-introduction) - Efficiency: The percent of the work put into a machine that is converted into useful work output; = work done / energy used [-]. = eta In machines: The work output of any machine cannot exceed the work input (<=100%); in an ideal machine, where no energy is transformed into heat: work(input) = work(output), =100%. Energy: The property of a system that enables it to do work. Conservation o. E.: Energy cannot be created or destroyed; it may be transformed from one form into another, but the total amount of energy never changes. Equilibrium: The state of an object when not acted upon by a net force or net torque; an object in equilibrium may be at rest or moving at uniform velocity - not accelerating. Mechanical E.: The state of an object or system of objects for which any impressed forces cancels to zero and no acceleration occurs. Dynamic E.: Object is moving without experiencing acceleration. Static E.: Object is at rest.F Force: The influence that can cause an object to be accelerated or retarded; is always in the direction of the net force, hence a vector quantity; the four elementary forces are: Electromagnetic F.: Is an attraction or repulsion G, gravit. const.6.672E-11[Nm2/kg2] between electric charges: d, distance [m] 2 2 2 2 F = 1/(40) (q1q2/d ) [(CC/m )(Nm /C )] = [N] m,M, mass [kg] Gravitational F.: Is a mutual attraction between all masses: q, charge [As] [C] 2 2 2 2 F = GmM/d [Nm /kg kg 1/m ] = [N] 0, dielectric constant Strong F.: (nuclear force) Acts within the nuclei of atoms: 8.854E-12 [C2/Nm2] [F/m] 2 2 2 2 2 F = 1/(40) (e /d ) [(CC/m )(Nm /C )] = [N] , 3.14 [-] Weak F.: Manifests itself in special reactions among elementary e, 1.60210 E-19 [As] [C] particles, such as the reaction that occur in radioactive decay. -
Forces Different Types of Forces
Forces and motion are a part of your everyday life for example pushing a trolley, a horse pulling a rope, speed and acceleration. Force and motion causes objects to move but also to stay still. Motion is simply a movement but needs a force to move. There are 2 types of forces, contact forces and act at a distance force. Forces Every day you are using forces. Force is basically push and pull. When you push and pull you are applying a force to an object. If you are Appling force to an object you are changing the objects motion. For an example when a ball is coming your way and then you push it away. The motion of the ball is changed because you applied a force. Different Types of Forces There are more forces than push or pull. Scientists group all these forces into two groups. The first group is contact forces, contact forces are forces when 2 objects are physically interacting with each other by touching. The second group is act at a distance force, act at a distance force is when 2 objects that are interacting with each other but not physically touching. Contact Forces There are different types of contact forces like normal Force, spring force, applied force and tension force. Normal force is when nothing is happening like a book lying on a table because gravity is pulling it down. Another contact force is spring force, spring force is created by a compressed or stretched spring that could push or pull. Applied force is when someone is applying a force to an object, for example a horse pulling a rope or a boy throwing a snow ball. -
USA Mathematical Talent Search Solutions to Problem 2/3/16
USA Mathematical Talent Search Solutions to Problem 2/3/16 www.usamts.org 2/3/16. Find three isosceles triangles, no two of which are congruent, with integer sides, such that each triangle’s area is numerically equal to 6 times its perimeter. Credit This is a slight modification of a problem provided by Suresh T. Thakar of India. The original problem asked for five isosceles triangles with integer sides such that the area is numerically 12 times the perimeter. Comments Many students simply set up an equation using Heron’s formula and then turned to a calculator or a computer for a solution. Below are presented more elegant solutions. Zachary Abel shows how to reduce this problem to finding Pythagorean triples which have 12 among the side lengths. Adam Hesterberg gives us a solution using Heron’s Create PDF with GO2PDFformula. for free, if Finally,you wish to remove Kristin this line, click Cordwell here to buy Virtual shows PDF Printer how to take an intelligent trial-and-error approach to construct the solutions. Solutions edited by Richard Rusczyk. Solution 1 by: Zachary Abel (11/TX) In 4ABC with AB = AC, we use the common notations r = inradius, s = semiperimeter, p = perimeter, K = area, a = BC, and b = AC. The diagram shows triangle ABC with its incircle centered at I and tangent to BC and AC at M and N respectively. A x h N 12 I a/2 12 B M a/2 C The area of the triangle is given by rs = K = 6p = 12s, which implies r = 12. -
Geodesic Distance Descriptors
Geodesic Distance Descriptors Gil Shamai and Ron Kimmel Technion - Israel Institute of Technologies [email protected] [email protected] Abstract efficiency of state of the art shape matching procedures. The Gromov-Hausdorff (GH) distance is traditionally used for measuring distances between metric spaces. It 1. Introduction was adapted for non-rigid shape comparison and match- One line of thought in shape analysis considers an ob- ing of isometric surfaces, and is defined as the minimal ject as a metric space, and object matching, classification, distortion of embedding one surface into the other, while and comparison as the operation of measuring the discrep- the optimal correspondence can be described as the map ancies and similarities between such metric spaces, see, for that minimizes this distortion. Solving such a minimiza- example, [13, 33, 27, 23, 8, 3, 24]. tion is a hard combinatorial problem that requires pre- Although theoretically appealing, the computation of computation and storing of all pairwise geodesic distances distances between metric spaces poses complexity chal- for the matched surfaces. A popular way for compact repre- lenges as far as direct computation and memory require- sentation of functions on surfaces is by projecting them into ments are involved. As a remedy, alternative representa- the leading eigenfunctions of the Laplace-Beltrami Opera- tion spaces were proposed [26, 22, 15, 10, 31, 30, 19, 20]. tor (LBO). When truncated, the basis of the LBO is known The question of which representation to use in order to best to be the optimal for representing functions with bounded represent the metric space that define each form we deal gradient in a min-max sense. -
Robust Topological Inference: Distance to a Measure and Kernel Distance
Journal of Machine Learning Research 18 (2018) 1-40 Submitted 9/15; Revised 2/17; Published 4/18 Robust Topological Inference: Distance To a Measure and Kernel Distance Fr´ed´ericChazal [email protected] Inria Saclay - Ile-de-France Alan Turing Bldg, Office 2043 1 rue Honor´ed'Estienne d'Orves 91120 Palaiseau, FRANCE Brittany Fasy [email protected] Computer Science Department Montana State University 357 EPS Building Montana State University Bozeman, MT 59717 Fabrizio Lecci [email protected] New York, NY Bertrand Michel [email protected] Ecole Centrale de Nantes Laboratoire de math´ematiquesJean Leray 1 Rue de La Noe 44300 Nantes FRANCE Alessandro Rinaldo [email protected] Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213 Larry Wasserman [email protected] Department of Statistics Carnegie Mellon University Pittsburgh, PA 15213 Editor: Mikhail Belkin Abstract Let P be a distribution with support S. The salient features of S can be quantified with persistent homology, which summarizes topological features of the sublevel sets of the dis- tance function (the distance of any point x to S). Given a sample from P we can infer the persistent homology using an empirical version of the distance function. However, the empirical distance function is highly non-robust to noise and outliers. Even one outlier is deadly. The distance-to-a-measure (DTM), introduced by Chazal et al. (2011), and the ker- nel distance, introduced by Phillips et al. (2014), are smooth functions that provide useful topological information but are robust to noise and outliers. Chazal et al. (2015) derived concentration bounds for DTM. -
Areas of Polygons and Circles
Chapter 8 Areas of Polygons and Circles Copyright © Cengage Learning. All rights reserved. Perimeter and Area of 8.2 Polygons Copyright © Cengage Learning. All rights reserved. Perimeter and Area of Polygons Definition The perimeter of a polygon is the sum of the lengths of all sides of the polygon. Table 8.1 summarizes perimeter formulas for types of triangles. 3 Perimeter and Area of Polygons Table 8.2 summarizes formulas for the perimeters of selected types of quadrilaterals. However, it is more important to understand the concept of perimeter than to memorize formulas. 4 Example 1 Find the perimeter of ABC shown in Figure 8.17 if: a) AB = 5 in., AC = 6 in., and BC = 7 in. b) AD = 8 cm, BC = 6 cm, and Solution: a) PABC = AB + AC + BC Figure 8.17 = 5 + 6 + 7 = 18 in. 5 Example 1 – Solution cont’d b) With , ABC is isosceles. Then is the bisector of If BC = 6, it follows that DC = 3. Using the Pythagorean Theorem, we have 2 2 2 (AD) + (DC) = (AC) 2 2 2 8 + 3 = (AC) 6 Example 1 – Solution cont’d 64 + 9 = (AC)2 AC = Now Note: Because x + x = 2x, we have 7 HERON’S FORMULA 8 Heron’s Formula If the lengths of the sides of a triangle are known, the formula generally used to calculate the area is Heron’s Formula. One of the numbers found in this formula is the semiperimeter of a triangle, which is defined as one-half the perimeter. For the triangle that has sides of lengths a, b, and c, the semiperimeter is s = (a + b + c). -
Figures Circumscribing Circles Tom M
Figures Circumscribing Circles Tom M. Apostol and Mamikon A. Mnatsakanian 1. INTRODUCTION. The centroid of the boundary of an arbitrarytriangle need not be at the same point as the centroid of its interior. But we have discovered that the two centroids are always collinear with the center of the inscribed circle, at distances in the ratio 3 : 2 from the center. We thought this charming fact must surely be known, but could find no mention of it in the literature. This paper generalizes this elegant and surprising result to any polygon that circumscribes a circle (Theorem 6). A key ingredient of the proof is a link to Archimedes' striking discovery concerning the area of a circular disk [4, p. 91], which for our purposes we prefer to state as follows: Theorem 1 (Archimedes). The area of a circular disk is equal to the product of its semiperimeter and its radius. Expressed as a formula, this becomes A = Pr, (1) where A is the area, P is the perimeter, and r is the radius of the disk. First we extend (1) to a large class of plane figures circumscribing a circle that we call circumgons, defined in section 2. They include arbitrarytriangles, all regular polygons, some irregularpolygons, and other figures composed of line segments and circular arcs. Examples are shown in Figures 1 through 4. Section 3 treats circum- gonal rings, plane regions lying between two similar circumgons. These rings have a constant width that replaces the radius in the corresponding extension of (1). We also show that all rings of constant width are necessarily circumgonal rings. -
On Asymmetric Distances
Analysis and Geometry in Metric Spaces Research Article • DOI: 10.2478/agms-2013-0004 • AGMS • 2013 • 200-231 On Asymmetric Distances Abstract ∗ In this paper we discuss asymmetric length structures and Andrea C. G. Mennucci asymmetric metric spaces. A length structure induces a (semi)distance function; by using Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126 Pisa, Italy the total variation formula, a (semi)distance function induces a length. In the first part we identify a topology in the set of paths that best describes when the above operations are idempo- tent. As a typical application, we consider the length of paths Received 24 March 2013 defined by a Finslerian functional in Calculus of Variations. Accepted 15 May 2013 In the second part we generalize the setting of General metric spaces of Busemann, and discuss the newly found aspects of the theory: we identify three interesting classes of paths, and compare them; we note that a geodesic segment (as defined by Busemann) is not necessarily continuous in our setting; hence we present three different notions of intrinsic metric space. Keywords Asymmetric metric • general metric • quasi metric • ostensible metric • Finsler metric • run–continuity • intrinsic metric • path metric • length structure MSC: 54C99, 54E25, 26A45 © Versita sp. z o.o. 1. Introduction Besides, one insists that the distance function be symmetric, that is, d(x; y) = d(y; x) (This unpleasantly limits many applications [...]) M. Gromov ([12], Intr.) The main purpose of this paper is to study an asymmetric metric theory; this theory naturally generalizes the metric part of Finsler Geometry, much as symmetric metric theory generalizes the metric part of Riemannian Geometry. -
Stml043-Endmatter.Pdf
http://dx.doi.org/10.1090/stml/043 STUDENT MATHEMATICAL LIBRARY Volume 43 Elementary Geometry Ilka Agricola Thomas Friedric h Translated by Philip G . Spain #AM^S^fcj S AMERICAN MATHEMATICA L SOCIET Y Providence, Rhode Islan d Editorial Boar d Gerald B . Follan d Bra d G . Osgoo d Robin Forma n (Chair ) Michae l Starbir d 2000 Mathematics Subject Classification. Primar y 51M04 , 51M09 , 51M15 . Originally publishe d i n Germa n b y Friedr . Viewe g & Sohn Verlag , 65189 Wiesbaden, Germany , a s "Ilk a Agricol a un d Thoma s Friedrich : Elementargeometrie. 1 . Auflag e (1s t edition)" . © Friedr . Viewe g & Sohn Verlag/GW V Fachverlag e GmbH , Wiesbaden , 2005 Translated b y Phili p G . Spai n For additiona l informatio n an d update s o n thi s book , visi t www.ams.org/bookpages/stml-43 Library o f Congress Cataloging-in-Publicatio n Dat a Agricola, Ilka , 1973- [Elementargeometrie. English ] Elementary geometr y / Ilk a Agricola , Thoma s Friedrich . p. cm . — (Student mathematica l librar y ; v. 43) Includes bibliographica l reference s an d index. ISBN-13 : 978-0-8218-4347- 5 (alk . paper ) ISBN-10 : 0-8218-4347- 8 (alk . paper ) 1. Geometry. I . Friedrich, Thomas , 1949 - II . Title. QA453.A37 200 7 516—dc22 200706084 4 Copying an d reprinting. Individua l reader s o f this publication , an d nonprofi t libraries actin g fo r them, ar e permitted t o mak e fai r us e of the material, suc h a s to copy a chapter fo r us e in teaching o r research. -
Hydraulics Manual Glossary G - 3
Glossary G - 1 GLOSSARY OF HIGHWAY-RELATED DRAINAGE TERMS (Reprinted from the 1999 edition of the American Association of State Highway and Transportation Officials Model Drainage Manual) G.1 Introduction This Glossary is divided into three parts: · Introduction, · Glossary, and · References. It is not intended that all the terms in this Glossary be rigorously accurate or complete. Realistically, this is impossible. Depending on the circumstance, a particular term may have several meanings; this can never change. The primary purpose of this Glossary is to define the terms found in the Highway Drainage Guidelines and Model Drainage Manual in a manner that makes them easier to interpret and understand. A lesser purpose is to provide a compendium of terms that will be useful for both the novice as well as the more experienced hydraulics engineer. This Glossary may also help those who are unfamiliar with highway drainage design to become more understanding and appreciative of this complex science as well as facilitate communication between the highway hydraulics engineer and others. Where readily available, the source of a definition has been referenced. For clarity or format purposes, cited definitions may have some additional verbiage contained in double brackets [ ]. Conversely, three “dots” (...) are used to indicate where some parts of a cited definition were eliminated. Also, as might be expected, different sources were found to use different hyphenation and terminology practices for the same words. Insignificant changes in this regard were made to some cited references and elsewhere to gain uniformity for the terms contained in this Glossary: as an example, “groundwater” vice “ground-water” or “ground water,” and “cross section area” vice “cross-sectional area.” Cited definitions were taken primarily from two sources: W.B. -
Mathematics of Distances and Applications
Michel Deza, Michel Petitjean, Krassimir Markov (eds.) Mathematics of Distances and Applications I T H E A SOFIA 2012 Michel Deza, Michel Petitjean, Krassimir Markov (eds.) Mathematics of Distances and Applications ITHEA® ISBN 978-954-16-0063-4 (printed); ISBN 978-954-16-0064-1 (online) ITHEA IBS ISC No.: 25 Sofia, Bulgaria, 2012 First edition Printed in Bulgaria Recommended for publication by The Scientific Council of the Institute of Information Theories and Applications FOI ITHEA The papers accepted and presented at the International Conference “Mathematics of Distances and Applications”, July 02-05, 2012, Varna, Bulgaria, are published in this book. The concept of distance is basic to human experience. In everyday life it usually means some degree of closeness of two physical objects or ideas, i.e., length, time interval, gap, rank difference, coolness or remoteness, while the term metric is often used as a standard for a measurement. Except for the last two topics, the mathematical meaning of those terms, which is an abstraction of measurement, is considered. Distance metrics and distances have now become an essential tool in many areas of Mathematics and its applications including Geometry, Probability, Statistics, Coding/Graph Theory, Clustering, Data Analysis, Pattern Recognition, Networks, Engineering, Computer Graphics/Vision, Astronomy, Cosmology, Molecular Biology, and many other areas of science. Devising the most suitable distance metrics and similarities, to quantify the proximity between objects, has become a standard task for many researchers. Especially intense ongoing search for such distances occurs, for example, in Computational Biology, Image Analysis, Speech Recognition, and Information Retrieval. For experts in the field of mathematics, information technologies as well as for practical users. -
Nonlinear Mapping and Distance Geometry
Nonlinear Mapping and Distance Geometry Alain Franc, Pierre Blanchard , Olivier Coulaud arXiv:1810.08661v2 [cs.CG] 9 May 2019 RESEARCH REPORT N° 9210 October 2018 Project-Teams Pleiade & ISSN 0249-6399 ISRN INRIA/RR--9210--FR+ENG HiePACS Nonlinear Mapping and Distance Geometry Alain Franc ∗†, Pierre Blanchard ∗ ‡, Olivier Coulaud ‡ Project-Teams Pleiade & HiePACS Research Report n° 9210 — version 2 — initial version October 2018 — revised version April 2019 — 14 pages Abstract: Distance Geometry Problem (DGP) and Nonlinear Mapping (NLM) are two well established questions: Distance Geometry Problem is about finding a Euclidean realization of an incomplete set of distances in a Euclidean space, whereas Nonlinear Mapping is a weighted Least Square Scaling (LSS) method. We show how all these methods (LSS, NLM, DGP) can be assembled in a common framework, being each identified as an instance of an optimization problem with a choice of a weight matrix. We study the continuity between the solutions (which are point clouds) when the weight matrix varies, and the compactness of the set of solutions (after centering). We finally study a numerical example, showing that solving the optimization problem is far from being simple and that the numerical solution for a given procedure may be trapped in a local minimum. Key-words: Distance Geometry; Nonlinear Mapping, Discrete Metric Space, Least Square Scaling; optimization ∗ BIOGECO, INRA, Univ. Bordeaux, 33610 Cestas, France † Pleiade team - INRIA Bordeaux-Sud-Ouest, France ‡ HiePACS team, Inria Bordeaux-Sud-Ouest,