Derivatives Using Limits, We Can Define the Slope of a Tangent Line to a Function. When Given a Function F(X)
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Infinitesimal and Tangent to Polylogarithmic Complexes For
AIMS Mathematics, 4(4): 1248–1257. DOI:10.3934/math.2019.4.1248 Received: 11 June 2019 Accepted: 11 August 2019 http://www.aimspress.com/journal/Math Published: 02 September 2019 Research article Infinitesimal and tangent to polylogarithmic complexes for higher weight Raziuddin Siddiqui* Department of Mathematical Sciences, Institute of Business Administration, Karachi, Pakistan * Correspondence: Email: [email protected]; Tel: +92-213-810-4700 Abstract: Motivic and polylogarithmic complexes have deep connections with K-theory. This article gives morphisms (different from Goncharov’s generalized maps) between |-vector spaces of Cathelineau’s infinitesimal complex for weight n. Our morphisms guarantee that the sequence of infinitesimal polylogs is a complex. We are also introducing a variant of Cathelineau’s complex with the derivation map for higher weight n and suggesting the definition of tangent group TBn(|). These tangent groups develop the tangent to Goncharov’s complex for weight n. Keywords: polylogarithm; infinitesimal complex; five term relation; tangent complex Mathematics Subject Classification: 11G55, 19D, 18G 1. Introduction The classical polylogarithms represented by Lin are one valued functions on a complex plane (see [11]). They are called generalization of natural logarithms, which can be represented by an infinite series (power series): X1 zk Li (z) = = − ln(1 − z) 1 k k=1 X1 zk Li (z) = 2 k2 k=1 : : X1 zk Li (z) = for z 2 ¼; jzj < 1 n kn k=1 The other versions of polylogarithms are Infinitesimal (see [8]) and Tangential (see [9]). We will discuss group theoretic form of infinitesimal and tangential polylogarithms in x 2.3, 2.4 and 2.5 below. -
An Introduction to Mathematical Modelling
An Introduction to Mathematical Modelling Glenn Marion, Bioinformatics and Statistics Scotland Given 2008 by Daniel Lawson and Glenn Marion 2008 Contents 1 Introduction 1 1.1 Whatismathematicalmodelling?. .......... 1 1.2 Whatobjectivescanmodellingachieve? . ............ 1 1.3 Classificationsofmodels . ......... 1 1.4 Stagesofmodelling............................... ....... 2 2 Building models 4 2.1 Gettingstarted .................................. ...... 4 2.2 Systemsanalysis ................................. ...... 4 2.2.1 Makingassumptions ............................. .... 4 2.2.2 Flowdiagrams .................................. 6 2.3 Choosingmathematicalequations. ........... 7 2.3.1 Equationsfromtheliterature . ........ 7 2.3.2 Analogiesfromphysics. ...... 8 2.3.3 Dataexploration ............................... .... 8 2.4 Solvingequations................................ ....... 9 2.4.1 Analytically.................................. .... 9 2.4.2 Numerically................................... 10 3 Studying models 12 3.1 Dimensionlessform............................... ....... 12 3.2 Asymptoticbehaviour ............................. ....... 12 3.3 Sensitivityanalysis . ......... 14 3.4 Modellingmodeloutput . ....... 16 4 Testing models 18 4.1 Testingtheassumptions . ........ 18 4.2 Modelstructure.................................. ...... 18 i 4.3 Predictionofpreviouslyunuseddata . ............ 18 4.3.1 Reasonsforpredictionerrors . ........ 20 4.4 Estimatingmodelparameters . ......... 20 4.5 Comparingtwomodelsforthesamesystem . ......... -
Example 10.8 Testing the Steady-State Approximation. ⊕ a B C C a B C P + → → + → D Dt K K K K K K [ ]
Example 10.8 Testing the steady-state approximation. Å The steady-state approximation contains an apparent contradiction: we set the time derivative of the concentration of some species (a reaction intermediate) equal to zero — implying that it is a constant — and then derive a formula showing how it changes with time. Actually, there is no contradiction since all that is required it that the rate of change of the "steady" species be small compared to the rate of reaction (as measured by the rate of disappearance of the reactant or appearance of the product). But exactly when (in a practical sense) is this approximation appropriate? It is often applied as a matter of convenience and justified ex post facto — that is, if the resulting rate law fits the data then the approximation is considered justified. But as this example demonstrates, such reasoning is dangerous and possible erroneous. We examine the mechanism A + B ¾¾1® C C ¾¾2® A + B (10.12) 3 C® P (Note that the second reaction is the reverse of the first, so we have a reversible second-order reaction followed by an irreversible first-order reaction.) The rate constants are k1 for the forward reaction of the first step, k2 for the reverse of the first step, and k3 for the second step. This mechanism is readily solved for with the steady-state approximation to give d[A] k1k3 = - ke[A][B] with ke = (10.13) dt k2 + k3 (TEXT Eq. (10.38)). With initial concentrations of A and B equal, hence [A] = [B] for all times, this equation integrates to 1 1 = + ket (10.14) [A] A0 where A0 is the initial concentration (equal to 1 in work to follow). -
Lecture 9: Partial Derivatives
Math S21a: Multivariable calculus Oliver Knill, Summer 2016 Lecture 9: Partial derivatives ∂ If f(x,y) is a function of two variables, then ∂x f(x,y) is defined as the derivative of the function g(x) = f(x,y) with respect to x, where y is considered a constant. It is called the partial derivative of f with respect to x. The partial derivative with respect to y is defined in the same way. ∂ We use the short hand notation fx(x,y) = ∂x f(x,y). For iterated derivatives, the notation is ∂ ∂ similar: for example fxy = ∂x ∂y f. The meaning of fx(x0,y0) is the slope of the graph sliced at (x0,y0) in the x direction. The second derivative fxx is a measure of concavity in that direction. The meaning of fxy is the rate of change of the slope if you change the slicing. The notation for partial derivatives ∂xf,∂yf was introduced by Carl Gustav Jacobi. Before, Josef Lagrange had used the term ”partial differences”. Partial derivatives fx and fy measure the rate of change of the function in the x or y directions. For functions of more variables, the partial derivatives are defined in a similar way. 4 2 2 4 3 2 2 2 1 For f(x,y)= x 6x y + y , we have fx(x,y)=4x 12xy ,fxx = 12x 12y ,fy(x,y)= − − − 12x2y +4y3,f = 12x2 +12y2 and see that f + f = 0. A function which satisfies this − yy − xx yy equation is also called harmonic. The equation fxx + fyy = 0 is an example of a partial differential equation: it is an equation for an unknown function f(x,y) which involves partial derivatives with respect to more than one variables. -
2. the Tangent Line
2. The Tangent Line The tangent line to a circle at a point P on its circumference is the line perpendicular to the radius of the circle at P. In Figure 1, The line T is the tangent line which is Tangent Line perpendicular to the radius of the circle at the point P. T Figure 1: A Tangent Line to a Circle While the tangent line to a circle has the property that it is perpendicular to the radius at the point of tangency, it is not this property which generalizes to other curves. We shall make an observation about the tangent line to the circle which is carried over to other curves, and may be used as its defining property. Let us look at a specific example. Let the equation of the circle in Figure 1 be x22 + y = 25. We may easily determine the equation of the tangent line to this circle at the point P(3,4). First, we observe that the radius is a segment of the line passing through the origin (0, 0) and P(3,4), and its equation is (why?). Since the tangent line is perpendicular to this line, its slope is -3/4 and passes through P(3, 4), using the point-slope formula, its equation is found to be Let us compute y-values on both the tangent line and the circle for x-values near the point P(3, 4). Note that near P, we can solve for the y-value on the upper half of the circle which is found to be When x = 3.01, we find the y-value on the tangent line is y = -3/4(3.01) + 25/4 = 3.9925, while the corresponding value on the circle is (Note that the tangent line lie above the circle, so its y-value was expected to be a larger.) In Table 1, we indicate other corresponding values as we vary x near P. -
Differentiation Rules (Differential Calculus)
Differentiation Rules (Differential Calculus) 1. Notation The derivative of a function f with respect to one independent variable (usually x or t) is a function that will be denoted by D f . Note that f (x) and (D f )(x) are the values of these functions at x. 2. Alternate Notations for (D f )(x) d d f (x) d f 0 (1) For functions f in one variable, x, alternate notations are: Dx f (x), dx f (x), dx , dx (x), f (x), f (x). The “(x)” part might be dropped although technically this changes the meaning: f is the name of a function, dy 0 whereas f (x) is the value of it at x. If y = f (x), then Dxy, dx , y , etc. can be used. If the variable t represents time then Dt f can be written f˙. The differential, “d f ”, and the change in f ,“D f ”, are related to the derivative but have special meanings and are never used to indicate ordinary differentiation. dy 0 Historical note: Newton used y,˙ while Leibniz used dx . About a century later Lagrange introduced y and Arbogast introduced the operator notation D. 3. Domains The domain of D f is always a subset of the domain of f . The conventional domain of f , if f (x) is given by an algebraic expression, is all values of x for which the expression is defined and results in a real number. If f has the conventional domain, then D f usually, but not always, has conventional domain. Exceptions are noted below. -
APPM 1350 - Calculus 1
APPM 1350 - Calculus 1 Course Objectives: This class will form the basis for many of the standard skills required in all of Engineering, the Sciences, and Mathematics. Specically, students will: • Understand the concepts, techniques and applications of dierential and integral calculus including limits, rate of change of functions, and derivatives and integrals of algebraic and transcendental functions. • Improve problem solving and critical thinking Textbook: Essential Calculus, 2nd Edition by James Stewart. We will cover Chapters 1-5. You will also need an access code for WebAssign’s online homework system. The access code can also be purchased separately. Schedule and Topics Covered Day Section Topics 1 Appendix A Trigonometry 2 1.1 Functions and Their Representation 3 1.2 Essential Functions 4 1.3 The Limit of a Function 5 1.4 Calculating Limits 6 1.5 Continuity 7 1.5/1.6 Continuity/Limits Involving Innity 8 1.6 Limits Involving Innity 9 2.1 Derivatives and Rates of Change 10 Exam 1 Review Exam 1 Topics 11 2.2 The Derivative as a Function 12 2.3 Basic Dierentiation Formulas 13 2.4 Product and Quotient Rules 14 2.5 Chain Rule 15 2.6 Implicit Dierentiation 16 2.7 Related Rates 17 2.8 Linear Approximation and Dierentials 18 3.1 Max and Min Values 19 3.2 The Mean Value Theorem 20 3.3 Derivatives and Shapes of Graphs 21 3.4 Curve Sketching 22 Exam 2 Review Exam 2 Topics 23 3.4/3.5 Curve Sketching/Optimization Problems 24 3.5 Optimization Problems 25 3.6/3.7 Newton’s Method/Antiderivatives 26 3.7/Appendix B Sigma Notation 27 Appendix B, -
A Quotient Rule Integration by Parts Formula Jennifer Switkes ([email protected]), California State Polytechnic Univer- Sity, Pomona, CA 91768
A Quotient Rule Integration by Parts Formula Jennifer Switkes ([email protected]), California State Polytechnic Univer- sity, Pomona, CA 91768 In a recent calculus course, I introduced the technique of Integration by Parts as an integration rule corresponding to the Product Rule for differentiation. I showed my students the standard derivation of the Integration by Parts formula as presented in [1]: By the Product Rule, if f (x) and g(x) are differentiable functions, then d f (x)g(x) = f (x)g(x) + g(x) f (x). dx Integrating on both sides of this equation, f (x)g(x) + g(x) f (x) dx = f (x)g(x), which may be rearranged to obtain f (x)g(x) dx = f (x)g(x) − g(x) f (x) dx. Letting U = f (x) and V = g(x) and observing that dU = f (x) dx and dV = g(x) dx, we obtain the familiar Integration by Parts formula UdV= UV − VdU. (1) My student Victor asked if we could do a similar thing with the Quotient Rule. While the other students thought this was a crazy idea, I was intrigued. Below, I derive a Quotient Rule Integration by Parts formula, apply the resulting integration formula to an example, and discuss reasons why this formula does not appear in calculus texts. By the Quotient Rule, if f (x) and g(x) are differentiable functions, then ( ) ( ) ( ) − ( ) ( ) d f x = g x f x f x g x . dx g(x) [g(x)]2 Integrating both sides of this equation, we get f (x) g(x) f (x) − f (x)g(x) = dx. -
1101 Calculus I Lecture 2.1: the Tangent and Velocity Problems
Calculus Lecture 2.1: The Tangent and Velocity Problems Page 1 1101 Calculus I Lecture 2.1: The Tangent and Velocity Problems The Tangent Problem A good way to think of what the tangent line to a curve is that it is a straight line which approximates the curve well in the region where it touches the curve. A more precise definition will be developed later. Recall, straight lines have equations y = mx + b (slope m, y-intercept b), or, more useful in this case, y − y0 = m(x − x0) (slope m, and passes through the point (x0, y0)). Your text has a fairly simple example. I will do something more complex instead. Example Find the tangent line to the parabola y = −3x2 + 12x − 8 at the point P (3, 1). Our solution involves finding the equation of a straight line, which is y − y0 = m(x − x0). We already know the tangent line should touch the curve, so it will pass through the point P (3, 1). This means x0 = 3 and y0 = 1. We now need to determine the slope of the tangent line, m. But we need two points to determine the slope of a line, and we only know one. We only know that the tangent line passes through the point P (3, 1). We proceed by approximations. We choose a point on the parabola that is nearby (3,1) and use it to approximate the slope of the tangent line. Let’s draw a sketch. Choose a point close to P (3, 1), say Q(4, −8). -
Laplace Transform
Chapter 7 Laplace Transform The Laplace transform can be used to solve differential equations. Be- sides being a different and efficient alternative to variation of parame- ters and undetermined coefficients, the Laplace method is particularly advantageous for input terms that are piecewise-defined, periodic or im- pulsive. The direct Laplace transform or the Laplace integral of a function f(t) defined for 0 ≤ t< 1 is the ordinary calculus integration problem 1 f(t)e−stdt; Z0 succinctly denoted L(f(t)) in science and engineering literature. The L{notation recognizes that integration always proceeds over t = 0 to t = 1 and that the integral involves an integrator e−stdt instead of the usual dt. These minor differences distinguish Laplace integrals from the ordinary integrals found on the inside covers of calculus texts. 7.1 Introduction to the Laplace Method The foundation of Laplace theory is Lerch's cancellation law 1 −st 1 −st 0 y(t)e dt = 0 f(t)e dt implies y(t)= f(t); (1) R R or L(y(t)= L(f(t)) implies y(t)= f(t): In differential equation applications, y(t) is the sought-after unknown while f(t) is an explicit expression taken from integral tables. Below, we illustrate Laplace's method by solving the initial value prob- lem y0 = −1; y(0) = 0: The method obtains a relation L(y(t)) = L(−t), whence Lerch's cancel- lation law implies the solution is y(t)= −t. The Laplace method is advertised as a table lookup method, in which the solution y(t) to a differential equation is found by looking up the answer in a special integral table. -
PLEASANTON UNIFIED SCHOOL DISTRICT Math 8/Algebra I Course Outline Form Course Title
PLEASANTON UNIFIED SCHOOL DISTRICT Math 8/Algebra I Course Outline Form Course Title: Math 8/Algebra I Course Number/CBED Number: Grade Level: 8 Length of Course: 1 year Credit: 10 Meets Graduation Requirements: n/a Required for Graduation: Prerequisite: Math 6/7 or Math 7 Course Description: Main concepts from the CCSS 8th grade content standards include: knowing that there are numbers that are not rational, and approximate them by rational numbers; working with radicals and integer exponents; understanding the connections between proportional relationships, lines, and linear equations; analyzing and solving linear equations and pairs of simultaneous linear equations; defining, evaluating, and comparing functions; using functions to model relationships between quantities; understanding congruence and similarity using physical models, transparencies, or geometry software; understanding and applying the Pythagorean Theorem; investigating patterns of association in bivariate data. Algebra (CCSS Algebra) main concepts include: reason quantitatively and use units to solve problems; create equations that describe numbers or relationships; understanding solving equations as a process of reasoning and explain reasoning; solve equations and inequalities in one variable; solve systems of equations; represent and solve equations and inequalities graphically; extend the properties of exponents to rational exponents; use properties of rational and irrational numbers; analyze and solve linear equations and pairs of simultaneous linear equations; define, -
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