Glossary of Requirements Engineering Terminology Caution: This Glossary Is Aligned to the CPRE Foundation Level Syllabus 3.0 Only!
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Common Course Outline MATH 257 Linear Algebra 4 Credits
Common Course Outline MATH 257 Linear Algebra 4 Credits The Community College of Baltimore County Description MATH 257 – Linear Algebra is one of the suggested elective courses for students majoring in Mathematics, Computer Science or Engineering. Included are geometric vectors, matrices, systems of linear equations, vector spaces, linear transformations, determinants, eigenvectors and inner product spaces. 4 Credits: 5 lecture hours Prerequisite: MATH 251 with a grade of “C” or better Overall Course Objectives Upon successfully completing the course, students will be able to: 1. perform matrix operations; 2. use Gaussian Elimination, Cramer’s Rule, and the inverse of the coefficient matrix to solve systems of Linear Equations; 3. find the inverse of a matrix by Gaussian Elimination or using the adjoint matrix; 4. compute the determinant of a matrix using cofactor expansion or elementary row operations; 5. apply Gaussian Elimination to solve problems concerning Markov Chains; 6. verify that a structure is a vector space by checking the axioms; 7. verify that a subset is a subspace and that a set of vectors is a basis; 8. compute the dimensions of subspaces; 9. compute the matrix representation of a linear transformation; 10. apply notions of linear transformations to discuss rotations and reflections of two dimensional space; 11. compute eigenvalues and find their corresponding eigenvectors; 12. diagonalize a matrix using eigenvalues; 13. apply properties of vectors and dot product to prove results in geometry; 14. apply notions of vectors, dot product and matrices to construct a best fitting curve; 15. construct a solution to real world problems using problem methods individually and in groups; 16. -
21. Orthonormal Bases
21. Orthonormal Bases The canonical/standard basis 011 001 001 B C B C B C B0C B1C B0C e1 = B.C ; e2 = B.C ; : : : ; en = B.C B.C B.C B.C @.A @.A @.A 0 0 1 has many useful properties. • Each of the standard basis vectors has unit length: q p T jjeijj = ei ei = ei ei = 1: • The standard basis vectors are orthogonal (in other words, at right angles or perpendicular). T ei ej = ei ej = 0 when i 6= j This is summarized by ( 1 i = j eT e = δ = ; i j ij 0 i 6= j where δij is the Kronecker delta. Notice that the Kronecker delta gives the entries of the identity matrix. Given column vectors v and w, we have seen that the dot product v w is the same as the matrix multiplication vT w. This is the inner product on n T R . We can also form the outer product vw , which gives a square matrix. 1 The outer product on the standard basis vectors is interesting. Set T Π1 = e1e1 011 B C B0C = B.C 1 0 ::: 0 B.C @.A 0 01 0 ::: 01 B C B0 0 ::: 0C = B. .C B. .C @. .A 0 0 ::: 0 . T Πn = enen 001 B C B0C = B.C 0 0 ::: 1 B.C @.A 1 00 0 ::: 01 B C B0 0 ::: 0C = B. .C B. .C @. .A 0 0 ::: 1 In short, Πi is the diagonal square matrix with a 1 in the ith diagonal position and zeros everywhere else. -
Effective Field Theories, Reductionism and Scientific Explanation Stephan
To appear in: Studies in History and Philosophy of Modern Physics Effective Field Theories, Reductionism and Scientific Explanation Stephan Hartmann∗ Abstract Effective field theories have been a very popular tool in quantum physics for almost two decades. And there are good reasons for this. I will argue that effec- tive field theories share many of the advantages of both fundamental theories and phenomenological models, while avoiding their respective shortcomings. They are, for example, flexible enough to cover a wide range of phenomena, and concrete enough to provide a detailed story of the specific mechanisms at work at a given energy scale. So will all of physics eventually converge on effective field theories? This paper argues that good scientific research can be characterised by a fruitful interaction between fundamental theories, phenomenological models and effective field theories. All of them have their appropriate functions in the research process, and all of them are indispens- able. They complement each other and hang together in a coherent way which I shall characterise in some detail. To illustrate all this I will present a case study from nuclear and particle physics. The resulting view about scientific theorising is inherently pluralistic, and has implications for the debates about reductionism and scientific explanation. Keywords: Effective Field Theory; Quantum Field Theory; Renormalisation; Reductionism; Explanation; Pluralism. ∗Center for Philosophy of Science, University of Pittsburgh, 817 Cathedral of Learning, Pitts- burgh, PA 15260, USA (e-mail: [email protected]) (correspondence address); and Sektion Physik, Universit¨at M¨unchen, Theresienstr. 37, 80333 M¨unchen, Germany. 1 1 Introduction There is little doubt that effective field theories are nowadays a very popular tool in quantum physics. -
Solving Cubic Polynomials
Solving Cubic Polynomials 1.1 The general solution to the quadratic equation There are four steps to finding the zeroes of a quadratic polynomial. 1. First divide by the leading term, making the polynomial monic. a 2. Then, given x2 + a x + a , substitute x = y − 1 to obtain an equation without the linear term. 1 0 2 (This is the \depressed" equation.) 3. Solve then for y as a square root. (Remember to use both signs of the square root.) a 4. Once this is done, recover x using the fact that x = y − 1 . 2 For example, let's solve 2x2 + 7x − 15 = 0: First, we divide both sides by 2 to create an equation with leading term equal to one: 7 15 x2 + x − = 0: 2 2 a 7 Then replace x by x = y − 1 = y − to obtain: 2 4 169 y2 = 16 Solve for y: 13 13 y = or − 4 4 Then, solving back for x, we have 3 x = or − 5: 2 This method is equivalent to \completing the square" and is the steps taken in developing the much- memorized quadratic formula. For example, if the original equation is our \high school quadratic" ax2 + bx + c = 0 then the first step creates the equation b c x2 + x + = 0: a a b We then write x = y − and obtain, after simplifying, 2a b2 − 4ac y2 − = 0 4a2 so that p b2 − 4ac y = ± 2a and so p b b2 − 4ac x = − ± : 2a 2a 1 The solutions to this quadratic depend heavily on the value of b2 − 4ac. -
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. -
An Introduction to Supersymmetry
An Introduction to Supersymmetry Ulrich Theis Institute for Theoretical Physics, Friedrich-Schiller-University Jena, Max-Wien-Platz 1, D–07743 Jena, Germany [email protected] This is a write-up of a series of five introductory lectures on global supersymmetry in four dimensions given at the 13th “Saalburg” Summer School 2007 in Wolfersdorf, Germany. Contents 1 Why supersymmetry? 1 2 Weyl spinors in D=4 4 3 The supersymmetry algebra 6 4 Supersymmetry multiplets 6 5 Superspace and superfields 9 6 Superspace integration 11 7 Chiral superfields 13 8 Supersymmetric gauge theories 17 9 Supersymmetry breaking 22 10 Perturbative non-renormalization theorems 26 A Sigma matrices 29 1 Why supersymmetry? When the Large Hadron Collider at CERN takes up operations soon, its main objective, besides confirming the existence of the Higgs boson, will be to discover new physics beyond the standard model of the strong and electroweak interactions. It is widely believed that what will be found is a (at energies accessible to the LHC softly broken) supersymmetric extension of the standard model. What makes supersymmetry such an attractive feature that the majority of the theoretical physics community is convinced of its existence? 1 First of all, under plausible assumptions on the properties of relativistic quantum field theories, supersymmetry is the unique extension of the algebra of Poincar´eand internal symmtries of the S-matrix. If new physics is based on such an extension, it must be supersymmetric. Furthermore, the quantum properties of supersymmetric theories are much better under control than in non-supersymmetric ones, thanks to powerful non- renormalization theorems. -
A Guiding Vector Field Algorithm for Path Following Control Of
1 A guiding vector field algorithm for path following control of nonholonomic mobile robots Yuri A. Kapitanyuk, Anton V. Proskurnikov and Ming Cao Abstract—In this paper we propose an algorithm for path- the integral tracking error is uniformly positive independent following control of the nonholonomic mobile robot based on the of the controller design. Furthermore, in practice the robot’s idea of the guiding vector field (GVF). The desired path may be motion along the trajectory can be quite “irregular” due to an arbitrary smooth curve in its implicit form, that is, a level set of a predefined smooth function. Using this function and the its oscillations around the reference point. For instance, it is robot’s kinematic model, we design a GVF, whose integral curves impossible to guarantee either path following at a precisely converge to the trajectory. A nonlinear motion controller is then constant speed, or even the motion with a perfectly fixed proposed which steers the robot along such an integral curve, direction. Attempting to keep close to the reference point, the bringing it to the desired path. We establish global convergence robot may “overtake” it due to unpredictable disturbances. conditions for our algorithm and demonstrate its applicability and performance by experiments with real wheeled robots. As has been clearly illustrated in the influential paper [11], these performance limitations of trajectory tracking can be Index Terms—Path following, vector field guidance, mobile removed by carefully designed path following algorithms. robot, motion control, nonlinear systems Unlike the tracking approach, path following control treats the path as a geometric curve rather than a function of I. -
Lesson 6: Trigonometric Identities
1. Introduction An identity is an equality relationship between two mathematical expressions. For example, in basic algebra students are expected to master various algbriac factoring identities such as a2 − b2 =(a − b)(a + b)or a3 + b3 =(a + b)(a2 − ab + b2): Identities such as these are used to simplifly algebriac expressions and to solve alge- a3 + b3 briac equations. For example, using the third identity above, the expression a + b simpliflies to a2 − ab + b2: The first identiy verifies that the equation (a2 − b2)=0is true precisely when a = b: The formulas or trigonometric identities introduced in this lesson constitute an integral part of the study and applications of trigonometry. Such identities can be used to simplifly complicated trigonometric expressions. This lesson contains several examples and exercises to demonstrate this type of procedure. Trigonometric identities can also used solve trigonometric equations. Equations of this type are introduced in this lesson and examined in more detail in Lesson 7. For student’s convenience, the identities presented in this lesson are sumarized in Appendix A 2. The Elementary Identities Let (x; y) be the point on the unit circle centered at (0; 0) that determines the angle t rad : Recall that the definitions of the trigonometric functions for this angle are sin t = y tan t = y sec t = 1 x y : cos t = x cot t = x csc t = 1 y x These definitions readily establish the first of the elementary or fundamental identities given in the table below. For obvious reasons these are often referred to as the reciprocal and quotient identities. -
CHAPTER 8. COMPLEX NUMBERS Why Do We Need Complex Numbers? First of All, a Simple Algebraic Equation Like X2 = −1 May Not Have
CHAPTER 8. COMPLEX NUMBERS Why do we need complex numbers? First of all, a simple algebraic equation like x2 = 1 may not have a real solution. − Introducing complex numbers validates the so called fundamental theorem of algebra: every polynomial with a positive degree has a root. However, the usefulness of complex numbers is much beyond such simple applications. Nowadays, complex numbers and complex functions have been developed into a rich theory called complex analysis and be- come a power tool for answering many extremely difficult questions in mathematics and theoretical physics, and also finds its usefulness in many areas of engineering and com- munication technology. For example, a famous result called the prime number theorem, which was conjectured by Gauss in 1849, and defied efforts of many great mathematicians, was finally proven by Hadamard and de la Vall´ee Poussin in 1896 by using the complex theory developed at that time. A widely quoted statement by Jacques Hadamard says: “The shortest path between two truths in the real domain passes through the complex domain”. The basic idea for complex numbers is to introduce a symbol i, called the imaginary unit, which satisfies i2 = 1. − In doing so, x2 = 1 turns out to have a solution, namely x = i; (actually, there − is another solution, namely x = i). We remark that, sometimes in the mathematical − literature, for convenience or merely following tradition, an incorrect expression with correct understanding is used, such as writing √ 1 for i so that we can reserve the − letter i for other purposes. But we try to avoid incorrect usage as much as possible. -
Determinants Math 122 Calculus III D Joyce, Fall 2012
Determinants Math 122 Calculus III D Joyce, Fall 2012 What they are. A determinant is a value associated to a square array of numbers, that square array being called a square matrix. For example, here are determinants of a general 2 × 2 matrix and a general 3 × 3 matrix. a b = ad − bc: c d a b c d e f = aei + bfg + cdh − ceg − afh − bdi: g h i The determinant of a matrix A is usually denoted jAj or det (A). You can think of the rows of the determinant as being vectors. For the 3×3 matrix above, the vectors are u = (a; b; c), v = (d; e; f), and w = (g; h; i). Then the determinant is a value associated to n vectors in Rn. There's a general definition for n×n determinants. It's a particular signed sum of products of n entries in the matrix where each product is of one entry in each row and column. The two ways you can choose one entry in each row and column of the 2 × 2 matrix give you the two products ad and bc. There are six ways of chosing one entry in each row and column in a 3 × 3 matrix, and generally, there are n! ways in an n × n matrix. Thus, the determinant of a 4 × 4 matrix is the signed sum of 24, which is 4!, terms. In this general definition, half the terms are taken positively and half negatively. In class, we briefly saw how the signs are determined by permutations. -
Towards a Glossary of Activities in the Ontology Engineering Field
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Servicio de Coordinación de Bibliotecas de la Universidad Politécnica de Madrid Towards a Glossary of Activities in the Ontology Engineering Field Mari Carmen Suárez-Figueroa, Asunción Gómez-Pérez Ontology Engineering Group, Universidad Politécnica de Madrid Campus de Montegancedo, Avda. Montepríncipe s/n, Boadilla del Monte, Madrid, Spain E-mail: [email protected], [email protected] Abstract The Semantic Web of the future will be characterized by using a very large number of ontologies embedded in ontology networks. It is important to provide strong methodological support for collaborative and context-sensitive development of networks of ontologies. This methodological support includes the identification and definition of which activities should be carried out when ontology networks are collaboratively built. In this paper we present the consensus reaching process followed within the NeOn consortium for the identification and definition of the activities involved in the ontology network development process. The consensus reaching process here presented produces as a result the NeOn Glossary of Activities. This work was conceived due to the lack of standardization in the Ontology Engineering terminology, which clearly contrasts with the Software Engineering field. Our future aim is to standardize the NeOn Glossary of Activities. field of knowledge. In the case of IEEE Standard Glossary 1. Introduction of Software Engineering Terminology (IEEE, 1990), this The Semantic Web of the future will be characterized by glossary identifies terms in use in the field of Software using a very large number of ontologies embedded in Engineering and establishes standard definitions for those ontology networks built by distributed teams (NeOn terms. -
28. Exterior Powers
28. Exterior powers 28.1 Desiderata 28.2 Definitions, uniqueness, existence 28.3 Some elementary facts 28.4 Exterior powers Vif of maps 28.5 Exterior powers of free modules 28.6 Determinants revisited 28.7 Minors of matrices 28.8 Uniqueness in the structure theorem 28.9 Cartan's lemma 28.10 Cayley-Hamilton Theorem 28.11 Worked examples While many of the arguments here have analogues for tensor products, it is worthwhile to repeat these arguments with the relevant variations, both for practice, and to be sensitive to the differences. 1. Desiderata Again, we review missing items in our development of linear algebra. We are missing a development of determinants of matrices whose entries may be in commutative rings, rather than fields. We would like an intrinsic definition of determinants of endomorphisms, rather than one that depends upon a choice of coordinates, even if we eventually prove that the determinant is independent of the coordinates. We anticipate that Artin's axiomatization of determinants of matrices should be mirrored in much of what we do here. We want a direct and natural proof of the Cayley-Hamilton theorem. Linear algebra over fields is insufficient, since the introduction of the indeterminate x in the definition of the characteristic polynomial takes us outside the class of vector spaces over fields. We want to give a conceptual proof for the uniqueness part of the structure theorem for finitely-generated modules over principal ideal domains. Multi-linear algebra over fields is surely insufficient for this. 417 418 Exterior powers 2. Definitions, uniqueness, existence Let R be a commutative ring with 1.