Convergence Rates for Deterministic and Stochastic Subgradient
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Section 8.8: Improper Integrals
Section 8.8: Improper Integrals One of the main applications of integrals is to compute the areas under curves, as you know. A geometric question. But there are some geometric questions which we do not yet know how to do by calculus, even though they appear to have the same form. Consider the curve y = 1=x2. We can ask, what is the area of the region under the curve and right of the line x = 1? We have no reason to believe this area is finite, but let's ask. Now no integral will compute this{we have to integrate over a bounded interval. Nonetheless, we don't want to throw up our hands. So note that b 2 b Z (1=x )dx = ( 1=x) 1 = 1 1=b: 1 − j − In other words, as b gets larger and larger, the area under the curve and above [1; b] gets larger and larger; but note that it gets closer and closer to 1. Thus, our intuition tells us that the area of the region we're interested in is exactly 1. More formally: lim 1 1=b = 1: b − !1 We can rewrite that as b 2 lim Z (1=x )dx: b !1 1 Indeed, in general, if we want to compute the area under y = f(x) and right of the line x = a, we are computing b lim Z f(x)dx: b !1 a ASK: Does this limit always exist? Give some situations where it does not exist. They'll give something that blows up. -
Notes on Calculus II Integral Calculus Miguel A. Lerma
Notes on Calculus II Integral Calculus Miguel A. Lerma November 22, 2002 Contents Introduction 5 Chapter 1. Integrals 6 1.1. Areas and Distances. The Definite Integral 6 1.2. The Evaluation Theorem 11 1.3. The Fundamental Theorem of Calculus 14 1.4. The Substitution Rule 16 1.5. Integration by Parts 21 1.6. Trigonometric Integrals and Trigonometric Substitutions 26 1.7. Partial Fractions 32 1.8. Integration using Tables and CAS 39 1.9. Numerical Integration 41 1.10. Improper Integrals 46 Chapter 2. Applications of Integration 50 2.1. More about Areas 50 2.2. Volumes 52 2.3. Arc Length, Parametric Curves 57 2.4. Average Value of a Function (Mean Value Theorem) 61 2.5. Applications to Physics and Engineering 63 2.6. Probability 69 Chapter 3. Differential Equations 74 3.1. Differential Equations and Separable Equations 74 3.2. Directional Fields and Euler’s Method 78 3.3. Exponential Growth and Decay 80 Chapter 4. Infinite Sequences and Series 83 4.1. Sequences 83 4.2. Series 88 4.3. The Integral and Comparison Tests 92 4.4. Other Convergence Tests 96 4.5. Power Series 98 4.6. Representation of Functions as Power Series 100 4.7. Taylor and MacLaurin Series 103 3 CONTENTS 4 4.8. Applications of Taylor Polynomials 109 Appendix A. Hyperbolic Functions 113 A.1. Hyperbolic Functions 113 Appendix B. Various Formulas 118 B.1. Summation Formulas 118 Appendix C. Table of Integrals 119 Introduction These notes are intended to be a summary of the main ideas in course MATH 214-2: Integral Calculus. -
Two Fundamental Theorems About the Definite Integral
Two Fundamental Theorems about the Definite Integral These lecture notes develop the theorem Stewart calls The Fundamental Theorem of Calculus in section 5.3. The approach I use is slightly different than that used by Stewart, but is based on the same fundamental ideas. 1 The definite integral Recall that the expression b f(x) dx ∫a is called the definite integral of f(x) over the interval [a,b] and stands for the area underneath the curve y = f(x) over the interval [a,b] (with the understanding that areas above the x-axis are considered positive and the areas beneath the axis are considered negative). In today's lecture I am going to prove an important connection between the definite integral and the derivative and use that connection to compute the definite integral. The result that I am eventually going to prove sits at the end of a chain of earlier definitions and intermediate results. 2 Some important facts about continuous functions The first intermediate result we are going to have to prove along the way depends on some definitions and theorems concerning continuous functions. Here are those definitions and theorems. The definition of continuity A function f(x) is continuous at a point x = a if the following hold 1. f(a) exists 2. lim f(x) exists xœa 3. lim f(x) = f(a) xœa 1 A function f(x) is continuous in an interval [a,b] if it is continuous at every point in that interval. The extreme value theorem Let f(x) be a continuous function in an interval [a,b]. -
Lipschitz Continuity of Convex Functions
Lipschitz Continuity of Convex Functions Bao Tran Nguyen∗ Pham Duy Khanh† November 13, 2019 Abstract We provide some necessary and sufficient conditions for a proper lower semi- continuous convex function, defined on a real Banach space, to be locally or globally Lipschitz continuous. Our criteria rely on the existence of a bounded selection of the subdifferential mapping and the intersections of the subdifferential mapping and the normal cone operator to the domain of the given function. Moreover, we also point out that the Lipschitz continuity of the given function on an open and bounded (not necessarily convex) set can be characterized via the existence of a bounded selection of the subdifferential mapping on the boundary of the given set and as a consequence it is equivalent to the local Lipschitz continuity at every point on the boundary of that set. Our results are applied to extend a Lipschitz and convex function to the whole space and to study the Lipschitz continuity of its Moreau envelope functions. Keywords Convex function, Lipschitz continuity, Calmness, Subdifferential, Normal cone, Moreau envelope function. Mathematics Subject Classification (2010) 26A16, 46N10, 52A41 1 Introduction arXiv:1911.04886v1 [math.FA] 12 Nov 2019 Lipschitz continuous and convex functions play a significant role in convex and nonsmooth analysis. It is well-known that if the domain of a proper lower semicontinuous convex function defined on a real Banach space has a nonempty interior then the function is continuous over the interior of its domain [3, Proposition 2.111] and as a consequence, it is subdifferentiable (its subdifferential is a nonempty set) and locally Lipschitz continuous at every point in the interior of its domain [3, Proposition 2.107]. -
The Beginnings 2 2. the Topology of Metric Spaces 5 3. Sequences and Completeness 9 4
MATH 3963 NONLINEAR ODES WITH APPLICATIONS R MARANGELL Contents 1. Metric Spaces - The Beginnings 2 2. The Topology of Metric Spaces 5 3. Sequences and Completeness 9 4. The Contraction Mapping Theorem 13 5. Lipschitz Continuity 19 6. Existence and Uniqueness of Solutions to ODEs 21 7. Dependence on Initial Conditions and Parameters 26 8. Maximal Intervals of Existence 29 Date: September 6, 2017. 1 R Marangell Part II - Existence and Uniqueness of ODES 1. Metric Spaces - The Beginnings So.... I am from California, and so I fly through L.A. a lot on my way to my parents house. How far is that from Sydney? Well... Google tells me it's 12000 km (give or take), and in fact this agrees with my airline - they very nicely give me 12000 km on my frequent flyer points. But.... well.... are they really 12000 km apart? What if I drilled a hole through the surface of the Earth? Using a little trigonometry, and the fact that the radius of the earth is R = 6371 km, we have that the distance `as the mole digs' so-to-speak from Sydney to Los Angeles CA is given by (see Figure 1): 12000 x = 2R sin ≈ 10303 km (give or take) 2R Figure 1. A schematic of the earth showing an `equatorial' (or great) circle from Sydney to L.A. So I have two different answers, to the same question, and both are correct. Indeed, Sydney is both 12000 km and around 10303 km away from L.A. What's going on here is pretty obvious, but we're going to make it precise. -
Lecture 13 Gradient Methods for Constrained Optimization
Lecture 13 Gradient Methods for Constrained Optimization October 16, 2008 Lecture 13 Outline • Gradient Projection Algorithm • Convergence Rate Convex Optimization 1 Lecture 13 Constrained Minimization minimize f(x) subject x ∈ X • Assumption 1: • The function f is convex and continuously differentiable over Rn • The set X is closed and convex ∗ • The optimal value f = infx∈Rn f(x) is finite • Gradient projection algorithm xk+1 = PX[xk − αk∇f(xk)] starting with x0 ∈ X. Convex Optimization 2 Lecture 13 Bounded Gradients Theorem 1 Let Assumption 1 hold, and suppose that the gradients are uniformly bounded over the set X. Then, the projection gradient method generates the sequence {xk} ⊂ X such that • When the constant stepsize αk ≡ α is used, we have 2 ∗ αL lim inf f(xk) ≤ f + k→∞ 2 P • When diminishing stepsize is used with k αk = +∞, we have ∗ lim inf f(xk) = f . k→∞ Proof: We use projection properties and the line of analysis similar to that of unconstrained method. HWK 6 Convex Optimization 3 Lecture 13 Lipschitz Gradients • Lipschitz Gradient Lemma For a differentiable convex function f with Lipschitz gradients, we have for all x, y ∈ Rn, 1 k∇f(x) − ∇f(y)k2 ≤ (∇f(x) − ∇f(y))T (x − y), L where L is a Lipschitz constant. • Theorem 2 Let Assumption 1 hold, and assume that the gradients of f are Lipschitz continuous over X. Suppose that the optimal solution ∗ set X is not empty. Then, for a constant stepsize αk ≡ α with 0 2 < α < L converges to an optimal point, i.e., ∗ ∗ ∗ lim kxk − x k = 0 for some x ∈ X . -
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 -
The Infinite and Contradiction: a History of Mathematical Physics By
The infinite and contradiction: A history of mathematical physics by dialectical approach Ichiro Ueki January 18, 2021 Abstract The following hypothesis is proposed: \In mathematics, the contradiction involved in the de- velopment of human knowledge is included in the form of the infinite.” To prove this hypothesis, the author tries to find what sorts of the infinite in mathematics were used to represent the con- tradictions involved in some revolutions in mathematical physics, and concludes \the contradiction involved in mathematical description of motion was represented with the infinite within recursive (computable) set level by early Newtonian mechanics; and then the contradiction to describe discon- tinuous phenomena with continuous functions and contradictions about \ether" were represented with the infinite higher than the recursive set level, namely of arithmetical set level in second or- der arithmetic (ordinary mathematics), by mechanics of continuous bodies and field theory; and subsequently the contradiction appeared in macroscopic physics applied to microscopic phenomena were represented with the further higher infinite in third or higher order arithmetic (set-theoretic mathematics), by quantum mechanics". 1 Introduction Contradictions found in set theory from the end of the 19th century to the beginning of the 20th, gave a shock called \a crisis of mathematics" to the world of mathematicians. One of the contradictions was reported by B. Russel: \Let w be the class [set]1 of all classes which are not members of themselves. Then whatever class x may be, 'x is a w' is equivalent to 'x is not an x'. Hence, giving to x the value w, 'w is a w' is equivalent to 'w is not a w'."[52] Russel described the crisis in 1959: I was led to this contradiction by Cantor's proof that there is no greatest cardinal number. -
The Lipschitz Constant of Self-Attention
The Lipschitz Constant of Self-Attention Hyunjik Kim 1 George Papamakarios 1 Andriy Mnih 1 Abstract constraint for neural networks, to control how much a net- Lipschitz constants of neural networks have been work’s output can change relative to its input. Such Lips- explored in various contexts in deep learning, chitz constraints are useful in several contexts. For example, such as provable adversarial robustness, estimat- Lipschitz constraints can endow models with provable ro- ing Wasserstein distance, stabilising training of bustness against adversarial pertubations (Cisse et al., 2017; GANs, and formulating invertible neural net- Tsuzuku et al., 2018; Anil et al., 2019), and guaranteed gen- works. Such works have focused on bounding eralisation bounds (Sokolic´ et al., 2017). Moreover, the dual the Lipschitz constant of fully connected or con- form of the Wasserstein distance is defined as a supremum volutional networks, composed of linear maps over Lipschitz functions with a given Lipschitz constant, and pointwise non-linearities. In this paper, we hence Lipschitz-constrained networks are used for estimat- investigate the Lipschitz constant of self-attention, ing Wasserstein distances (Peyré & Cuturi, 2019). Further, a non-linear neural network module widely used Lipschitz-constrained networks can stabilise training for in sequence modelling. We prove that the stan- GANs, an example being spectral normalisation (Miyato dard dot-product self-attention is not Lipschitz et al., 2018). Finally, Lipschitz-constrained networks are for unbounded input domain, and propose an al- also used to construct invertible models and normalising ternative L2 self-attention that is Lipschitz. We flows. For example, Lipschitz-constrained networks can be derive an upper bound on the Lipschitz constant of used as a building block for invertible residual networks and L2 self-attention and provide empirical evidence hence flow-based generative models (Behrmann et al., 2019; for its asymptotic tightness. -
Mean Value, Taylor, and All That
Mean Value, Taylor, and all that Ambar N. Sengupta Louisiana State University November 2009 Careful: Not proofread! Derivative Recall the definition of the derivative of a function f at a point p: f (w) − f (p) f 0(p) = lim (1) w!p w − p Derivative Thus, to say that f 0(p) = 3 means that if we take any neighborhood U of 3, say the interval (1; 5), then the ratio f (w) − f (p) w − p falls inside U when w is close enough to p, i.e. in some neighborhood of p. (Of course, we can’t let w be equal to p, because of the w − p in the denominator.) In particular, f (w) − f (p) > 0 if w is close enough to p, but 6= p. w − p Derivative So if f 0(p) = 3 then the ratio f (w) − f (p) w − p lies in (1; 5) when w is close enough to p, i.e. in some neighborhood of p, but not equal to p. Derivative So if f 0(p) = 3 then the ratio f (w) − f (p) w − p lies in (1; 5) when w is close enough to p, i.e. in some neighborhood of p, but not equal to p. In particular, f (w) − f (p) > 0 if w is close enough to p, but 6= p. w − p • when w > p, but near p, the value f (w) is > f (p). • when w < p, but near p, the value f (w) is < f (p). Derivative From f 0(p) = 3 we found that f (w) − f (p) > 0 if w is close enough to p, but 6= p. -
MATH 162: Calculus II Differentiation
MATH 162: Calculus II Framework for Mon., Jan. 29 Review of Differentiation and Integration Differentiation Definition of derivative f 0(x): f(x + h) − f(x) f(y) − f(x) lim or lim . h→0 h y→x y − x Differentiation rules: 1. Sum/Difference rule: If f, g are differentiable at x0, then 0 0 0 (f ± g) (x0) = f (x0) ± g (x0). 2. Product rule: If f, g are differentiable at x0, then 0 0 0 (fg) (x0) = f (x0)g(x0) + f(x0)g (x0). 3. Quotient rule: If f, g are differentiable at x0, and g(x0) 6= 0, then 0 0 0 f f (x0)g(x0) − f(x0)g (x0) (x0) = 2 . g [g(x0)] 4. Chain rule: If g is differentiable at x0, and f is differentiable at g(x0), then 0 0 0 (f ◦ g) (x0) = f (g(x0))g (x0). This rule may also be expressed as dy dy du = . dx x=x0 du u=u(x0) dx x=x0 Implicit differentiation is a consequence of the chain rule. For instance, if y is really dependent upon x (i.e., y = y(x)), and if u = y3, then d du du dy d (y3) = = = (y3)y0(x) = 3y2y0. dx dx dy dx dy Practice: Find d x d √ d , (x2 y), and [y cos(xy)]. dx y dx dx MATH 162—Framework for Mon., Jan. 29 Review of Differentiation and Integration Integration The definite integral • the area problem • Riemann sums • definition Fundamental Theorem of Calculus: R x I: Suppose f is continuous on [a, b]. -
Hölder-Continuity for the Nonlinear Stochastic Heat Equation with Rough Initial Conditions
Stoch PDE: Anal Comp (2014) 2:316–352 DOI 10.1007/s40072-014-0034-6 Hölder-continuity for the nonlinear stochastic heat equation with rough initial conditions Le Chen · Robert C. Dalang Received: 22 October 2013 / Published online: 14 August 2014 © Springer Science+Business Media New York 2014 Abstract We study space-time regularity of the solution of the nonlinear stochastic heat equation in one spatial dimension driven by space-time white noise, with a rough initial condition. This initial condition is a locally finite measure μ with, possibly, exponentially growing tails. We show how this regularity depends, in a neighborhood of t = 0, on the regularity of the initial condition. On compact sets in which t > 0, 1 − 1 − the classical Hölder-continuity exponents 4 in time and 2 in space remain valid. However, on compact sets that include t = 0, the Hölder continuity of the solution is α ∧ 1 − α ∧ 1 − μ 2 4 in time and 2 in space, provided is absolutely continuous with an α-Hölder continuous density. Keywords Nonlinear stochastic heat equation · Rough initial data · Sample path Hölder continuity · Moments of increments Mathematics Subject Classification Primary 60H15 · Secondary 60G60 · 35R60 L. Chen and R. C. Dalang were supported in part by the Swiss National Foundation for Scientific Research. L. Chen (B) · R. C. Dalang Institut de mathématiques, École Polytechnique Fédérale de Lausanne, Station 8, CH-1015 Lausanne, Switzerland e-mail: [email protected] R. C. Dalang e-mail: robert.dalang@epfl.ch Present Address: L. Chen Department of Mathematics, University of Utah, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA 123 Stoch PDE: Anal Comp (2014) 2:316–352 317 1 Introduction Over the last few years, there has been considerable interest in the stochastic heat equation with non-smooth initial data: ∂ ν ∂2 ˙ ∗ − u(t, x) = ρ(u(t, x)) W(t, x), x ∈ R, t ∈ R+, ∂t 2 ∂x2 (1.1) u(0, ·) = μ(·).