Lecture 1, 9/8/2010 the Laplace Operator in Rn Is ∆ = ∂2 ∂X2 +
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Arxiv:1507.07356V2 [Math.AP]
TEN EQUIVALENT DEFINITIONS OF THE FRACTIONAL LAPLACE OPERATOR MATEUSZ KWAŚNICKI Abstract. This article discusses several definitions of the fractional Laplace operator ( ∆)α/2 (α (0, 2)) in Rd (d 1), also known as the Riesz fractional derivative − ∈ ≥ operator, as an operator on Lebesgue spaces L p (p [1, )), on the space C0 of ∈ ∞ continuous functions vanishing at infinity and on the space Cbu of bounded uniformly continuous functions. Among these definitions are ones involving singular integrals, semigroups of operators, Bochner’s subordination and harmonic extensions. We collect and extend known results in order to prove that all these definitions agree: on each of the function spaces considered, the corresponding operators have common domain and they coincide on that common domain. 1. Introduction We consider the fractional Laplace operator L = ( ∆)α/2 in Rd, with α (0, 2) and d 1, 2, ... Numerous definitions of L can be found− in− literature: as a Fourier∈ multiplier with∈{ symbol} ξ α, as a fractional power in the sense of Bochner or Balakrishnan, as the inverse of the−| Riesz| potential operator, as a singular integral operator, as an operator associated to an appropriate Dirichlet form, as an infinitesimal generator of an appropriate semigroup of contractions, or as the Dirichlet-to-Neumann operator for an appropriate harmonic extension problem. Equivalence of these definitions for sufficiently smooth functions is well-known and easy. The purpose of this article is to prove that, whenever meaningful, all these definitions are equivalent in the Lebesgue space L p for p [1, ), ∈ ∞ in the space C0 of continuous functions vanishing at infinity, and in the space Cbu of bounded uniformly continuous functions. -
The Mean Value Theorem Math 120 Calculus I Fall 2015
The Mean Value Theorem Math 120 Calculus I Fall 2015 The central theorem to much of differential calculus is the Mean Value Theorem, which we'll abbreviate MVT. It is the theoretical tool used to study the first and second derivatives. There is a nice logical sequence of connections here. It starts with the Extreme Value Theorem (EVT) that we looked at earlier when we studied the concept of continuity. It says that any function that is continuous on a closed interval takes on a maximum and a minimum value. A technical lemma. We begin our study with a technical lemma that allows us to relate 0 the derivative of a function at a point to values of the function nearby. Specifically, if f (x0) is positive, then for x nearby but smaller than x0 the values f(x) will be less than f(x0), but for x nearby but larger than x0, the values of f(x) will be larger than f(x0). This says something like f is an increasing function near x0, but not quite. An analogous statement 0 holds when f (x0) is negative. Proof. The proof of this lemma involves the definition of derivative and the definition of limits, but none of the proofs for the rest of the theorems here require that depth. 0 Suppose that f (x0) = p, some positive number. That means that f(x) − f(x ) lim 0 = p: x!x0 x − x0 f(x) − f(x0) So you can make arbitrarily close to p by taking x sufficiently close to x0. -
AP Calculus AB Topic List 1. Limits Algebraically 2. Limits Graphically 3
AP Calculus AB Topic List 1. Limits algebraically 2. Limits graphically 3. Limits at infinity 4. Asymptotes 5. Continuity 6. Intermediate value theorem 7. Differentiability 8. Limit definition of a derivative 9. Average rate of change (approximate slope) 10. Tangent lines 11. Derivatives rules and special functions 12. Chain Rule 13. Application of chain rule 14. Derivatives of generic functions using chain rule 15. Implicit differentiation 16. Related rates 17. Derivatives of inverses 18. Logarithmic differentiation 19. Determine function behavior (increasing, decreasing, concavity) given a function 20. Determine function behavior (increasing, decreasing, concavity) given a derivative graph 21. Interpret first and second derivative values in a table 22. Determining if tangent line approximations are over or under estimates 23. Finding critical points and determining if they are relative maximum, relative minimum, or neither 24. Second derivative test for relative maximum or minimum 25. Finding inflection points 26. Finding and justifying critical points from a derivative graph 27. Absolute maximum and minimum 28. Application of maximum and minimum 29. Motion derivatives 30. Vertical motion 31. Mean value theorem 32. Approximating area with rectangles and trapezoids given a function 33. Approximating area with rectangles and trapezoids given a table of values 34. Determining if area approximations are over or under estimates 35. Finding definite integrals graphically 36. Finding definite integrals using given integral values 37. Indefinite integrals with power rule or special derivatives 38. Integration with u-substitution 39. Evaluating definite integrals 40. Definite integrals with u-substitution 41. Solving initial value problems (separable differential equations) 42. Creating a slope field 43. -
Curl, Divergence and Laplacian
Curl, Divergence and Laplacian What to know: 1. The definition of curl and it two properties, that is, theorem 1, and be able to predict qualitatively how the curl of a vector field behaves from a picture. 2. The definition of divergence and it two properties, that is, if div F~ 6= 0 then F~ can't be written as the curl of another field, and be able to tell a vector field of clearly nonzero,positive or negative divergence from the picture. 3. Know the definition of the Laplace operator 4. Know what kind of objects those operator take as input and what they give as output. The curl operator Let's look at two plots of vector fields: Figure 1: The vector field Figure 2: The vector field h−y; x; 0i: h1; 1; 0i We can observe that the second one looks like it is rotating around the z axis. We'd like to be able to predict this kind of behavior without having to look at a picture. We also promised to find a criterion that checks whether a vector field is conservative in R3. Both of those goals are accomplished using a tool called the curl operator, even though neither of those two properties is exactly obvious from the definition we'll give. Definition 1. Let F~ = hP; Q; Ri be a vector field in R3, where P , Q and R are continuously differentiable. We define the curl operator: @R @Q @P @R @Q @P curl F~ = − ~i + − ~j + − ~k: (1) @y @z @z @x @x @y Remarks: 1. -
Multivariable and Vector Calculus
Multivariable and Vector Calculus Lecture Notes for MATH 0200 (Spring 2015) Frederick Tsz-Ho Fong Department of Mathematics Brown University Contents 1 Three-Dimensional Space ....................................5 1.1 Rectangular Coordinates in R3 5 1.2 Dot Product7 1.3 Cross Product9 1.4 Lines and Planes 11 1.5 Parametric Curves 13 2 Partial Differentiations ....................................... 19 2.1 Functions of Several Variables 19 2.2 Partial Derivatives 22 2.3 Chain Rule 26 2.4 Directional Derivatives 30 2.5 Tangent Planes 34 2.6 Local Extrema 36 2.7 Lagrange’s Multiplier 41 2.8 Optimizations 46 3 Multiple Integrations ........................................ 49 3.1 Double Integrals in Rectangular Coordinates 49 3.2 Fubini’s Theorem for General Regions 53 3.3 Double Integrals in Polar Coordinates 57 3.4 Triple Integrals in Rectangular Coordinates 62 3.5 Triple Integrals in Cylindrical Coordinates 67 3.6 Triple Integrals in Spherical Coordinates 70 4 Vector Calculus ............................................ 75 4.1 Vector Fields on R2 and R3 75 4.2 Line Integrals of Vector Fields 83 4.3 Conservative Vector Fields 88 4.4 Green’s Theorem 98 4.5 Parametric Surfaces 105 4.6 Stokes’ Theorem 120 4.7 Divergence Theorem 127 5 Topics in Physics and Engineering .......................... 133 5.1 Coulomb’s Law 133 5.2 Introduction to Maxwell’s Equations 137 5.3 Heat Diffusion 141 5.4 Dirac Delta Functions 144 1 — Three-Dimensional Space 1.1 Rectangular Coordinates in R3 Throughout the course, we will use an ordered triple (x, y, z) to represent a point in the three dimensional space. -
Concavity and Points of Inflection We Now Know How to Determine Where a Function Is Increasing Or Decreasing
Chapter 4 | Applications of Derivatives 401 4.17 3 Use the first derivative test to find all local extrema for f (x) = x − 1. Concavity and Points of Inflection We now know how to determine where a function is increasing or decreasing. However, there is another issue to consider regarding the shape of the graph of a function. If the graph curves, does it curve upward or curve downward? This notion is called the concavity of the function. Figure 4.34(a) shows a function f with a graph that curves upward. As x increases, the slope of the tangent line increases. Thus, since the derivative increases as x increases, f ′ is an increasing function. We say this function f is concave up. Figure 4.34(b) shows a function f that curves downward. As x increases, the slope of the tangent line decreases. Since the derivative decreases as x increases, f ′ is a decreasing function. We say this function f is concave down. Definition Let f be a function that is differentiable over an open interval I. If f ′ is increasing over I, we say f is concave up over I. If f ′ is decreasing over I, we say f is concave down over I. Figure 4.34 (a), (c) Since f ′ is increasing over the interval (a, b), we say f is concave up over (a, b). (b), (d) Since f ′ is decreasing over the interval (a, b), we say f is concave down over (a, b). 402 Chapter 4 | Applications of Derivatives In general, without having the graph of a function f , how can we determine its concavity? By definition, a function f is concave up if f ′ is increasing. -
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 -
Matrix Calculus
Appendix D Matrix Calculus From too much study, and from extreme passion, cometh madnesse. Isaac Newton [205, §5] − D.1 Gradient, Directional derivative, Taylor series D.1.1 Gradients Gradient of a differentiable real function f(x) : RK R with respect to its vector argument is defined uniquely in terms of partial derivatives→ ∂f(x) ∂x1 ∂f(x) , ∂x2 RK f(x) . (2053) ∇ . ∈ . ∂f(x) ∂xK while the second-order gradient of the twice differentiable real function with respect to its vector argument is traditionally called the Hessian; 2 2 2 ∂ f(x) ∂ f(x) ∂ f(x) 2 ∂x1 ∂x1∂x2 ··· ∂x1∂xK 2 2 2 ∂ f(x) ∂ f(x) ∂ f(x) 2 2 K f(x) , ∂x2∂x1 ∂x2 ··· ∂x2∂xK S (2054) ∇ . ∈ . .. 2 2 2 ∂ f(x) ∂ f(x) ∂ f(x) 2 ∂xK ∂x1 ∂xK ∂x2 ∂x ··· K interpreted ∂f(x) ∂f(x) 2 ∂ ∂ 2 ∂ f(x) ∂x1 ∂x2 ∂ f(x) = = = (2055) ∂x1∂x2 ³∂x2 ´ ³∂x1 ´ ∂x2∂x1 Dattorro, Convex Optimization Euclidean Distance Geometry, Mεβoo, 2005, v2020.02.29. 599 600 APPENDIX D. MATRIX CALCULUS The gradient of vector-valued function v(x) : R RN on real domain is a row vector → v(x) , ∂v1(x) ∂v2(x) ∂vN (x) RN (2056) ∇ ∂x ∂x ··· ∂x ∈ h i while the second-order gradient is 2 2 2 2 , ∂ v1(x) ∂ v2(x) ∂ vN (x) RN v(x) 2 2 2 (2057) ∇ ∂x ∂x ··· ∂x ∈ h i Gradient of vector-valued function h(x) : RK RN on vector domain is → ∂h1(x) ∂h2(x) ∂hN (x) ∂x1 ∂x1 ··· ∂x1 ∂h1(x) ∂h2(x) ∂hN (x) h(x) , ∂x2 ∂x2 ··· ∂x2 ∇ . -
440 Geophysics: Brief Notes on Math Thorsten Becker, University of Southern California, 02/2005
440 Geophysics: Brief notes on math Thorsten Becker, University of Southern California, 02/2005 Linear algebra Linear (vector) algebra is discussed in the appendix of Fowler and in your favorite math textbook. I only make brief comments here, to clarify and summarize a few issues that came up in class. At places, these are not necessarily mathematically rigorous. Dot product We have made use of the dot product, which is defined as n c =~a ·~b = ∑ aibi, (1) i=1 where ~a and~b are vectors of dimension n (n-dimensional, pointed objects like a velocity) and the n outcome of this operation is a scalar (a regular number), c. In eq. (1), ∑i=1 means “sum all that follows while increasing the index i from the lower limit, i = 1, in steps of of unity, to the upper limit, i = n”. In the examples below, we will assume a typical, spatial coordinate system with n = 3 so that ~a ·~b = a1b1 + a2b2 + a3b3, (2) where 1, 2, 3 refer to the vectors components along x, y, and z axis, respectively. When we write out the vector components, we put them on top of each other a1 ax ~a = a2 = ay (3) a3 az or in a list, maybe with curly brackets, like so: ~a = {a1,a2,a3}. On the board, I usually write vectors as a rather than ~a, because that’s easier. You may also see vectors printed as bold face letters, like so: a. We can write the amplitude or length of a vector as s n q q 2 2 2 2 2 2 2 |~a| = ∑ai = a1 + a2 + a3 = ax + ay + az . -
3.4 AP Calculus Concavity and the Second Derivative.Notebook November 07, 2016
3.4 AP Calculus Concavity and the second derivative.notebook November 07, 2016 3.4 Concavity & The Second Derivative Learning Targets 1. Determine intervals on which a function is concave upward or concave downward 2. Find inflection points of a function. 3. Find relative extrema of a function using Second Derivative Test. *no make‐up Monday today Intro/Warmup 1 3.4 AP Calculus Concavity and the second derivative.notebook November 07, 2016 Nov 78:41 AM 2 3.4 AP Calculus Concavity and the second derivative.notebook November 07, 2016 ∫ Concave Upward or Concave Downward If f' is increasing on an interval, then the function is concave upward on that interval If f' is decreasing on an interval, then the function is concave downward on that interval. note: concave upward means the graph lies above its tangent lines, and concave downwards means the graph lies below its tangent lines. I.concave upward or concave downward 3 3.4 AP Calculus Concavity and the second derivative.notebook November 07, 2016 ∫ Concave Upward or Concave Downward continued Test For Concavity: Let f be a function whose second derivative exists on an open interval I. 1. If f''(x) > 0 for all x in I, then the graph of f is concave upward on I. 2. If f''(x) < 0 for all x in I, then the graph of f is concave downward on I. 1. Determine the open intervals on which the graph of is concave upward or downward. I.Concave Upward or Downward cont 4 3.4 AP Calculus Concavity and the second derivative.notebook November 07, 2016 ∫∫∫ Second Derivative Test Second Derivative Test Let f be a function such that f'(c) = 0 and the second derivative of f exists on an open interval containing c. -
4.1 Discrete Differential Geometry
Spring 2015 CSCI 599: Digital Geometry Processing 4.1 Discrete Differential Geometry Hao Li http://cs599.hao-li.com 1 Outline • Discrete Differential Operators • Discrete Curvatures • Mesh Quality Measures 2 Differential Operators on Polygons Differential Properties! • Surface is sufficiently differentiable • Curvatures → 2nd derivatives 3 Differential Operators on Polygons Differential Properties! • Surface is sufficiently differentiable • Curvatures → 2nd derivatives Polygonal Meshes! • Piecewise linear approximations of smooth surface • Focus on Discrete Laplace Beltrami Operator • Discrete differential properties defined over (x) N 4 Local Averaging Local Neighborhood ( x ) of a point !x N • often coincides with mesh vertex vi • n-ring neighborhood ( v ) or local geodesic ball Nn i 5 Local Averaging Local Neighborhood ( x ) of a point !x N • often coincides with mesh vertex vi • n-ring neighborhood ( v ) or local geodesic ball Nn i Neighborhood size! • Large: smoothing is introduced, stable to noise • Small: fine scale variation, sensitive to noise 6 Local Averaging: 1-Ring (x) N x Barycentric cell Voronoi cell Mixed Voronoi cell (barycenters/edgemidpoints) (circumcenters) (circumcenters/midpoint) tight error bound better approximation 7 Barycentric Cells Connect edge midpoints and triangle barycenters! • Simple to compute • Area is 1/3 o triangle areas • Slightly wrong for obtuse triangles 8 Mixed Cells Connect edge midpoints and! • Circumcenters for non-obtuse triangles • Midpoint of opposite edge for obtuse triangles • Better approximation, -
A Doubly Nonlocal Laplace Operator and Its Connection to the Classical Laplacian Petronela Radu University of Nebraska - Lincoln, [email protected]
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Faculty Publications, Department of Mathematics Mathematics, Department of 2019 A Doubly Nonlocal Laplace Operator and Its Connection to the Classical Laplacian Petronela Radu University of Nebraska - Lincoln, [email protected] Kelseys Wells University of Nebraska - Lincoln Follow this and additional works at: https://digitalcommons.unl.edu/mathfacpub Part of the Applied Mathematics Commons, and the Mathematics Commons Radu, Petronela and Wells, Kelseys, "A Doubly Nonlocal Laplace Operator and Its Connection to the Classical Laplacian" (2019). Faculty Publications, Department of Mathematics. 215. https://digitalcommons.unl.edu/mathfacpub/215 This Article is brought to you for free and open access by the Mathematics, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Faculty Publications, Department of Mathematics by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. A DOUBLY NONLOCAL LAPLACE OPERATOR AND ITS CONNECTION TO THE CLASSICAL LAPLACIAN PETRONELA RADU, KELSEY WELLS Abstract. In this paper, motivated by the state-based peridynamic frame- work, we introduce a new nonlocal Laplacian that exhibits double nonlocality through the use of iterated integral operators. The operator introduces addi- tional degrees of flexibility that can allow for better representation of physical phenomena at different scales and in materials with different properties. We study mathematical properties of this state-based Laplacian, including connec- tions with other nonlocal and local counterparts. Finally, we obtain explicit rates of convergence for this doubly nonlocal operator to the classical Laplacian as the radii for the horizons of interaction kernels shrink to zero. 1. Introduction Physical phenomena that are beset by discontinuities in the solution, or in the domain, have been challenging to study in the context of classical partial differen- tial equations (PDEs).