This document provides information about exponential functions:
- Exponential functions are defined as f(x) = ax, where a is a positive number called the base.
- The graphs of exponential functions with a base greater than 1 grow exponentially to the right, while graphs with a base between 0 and 1 slope down as they move to the right.
- Exponential functions are one-to-one and onto, appearing in processes like population growth, interest rates, and carbon dating.
The document discusses linear transformations and linear independence. It contains examples and explanations of:
1) How a matrix A can transform a vector x from R4 to a new vector b in R2, representing the linear transformation.
2) How finding vectors x such that Ax=b is equivalent to finding pre-images of b under the transformation A.
3) Key concepts related to linear transformations like domain and range.
At times it is useful to consider a function whose derivative is a given function. We look at the general idea of reversing the differentiation process and its applications to rectilinear motion.
Lesson 16: Derivatives of Logarithmic and Exponential FunctionsMatthew Leingang
We show the the derivative of the exponential function is itself! And the derivative of the natural logarithm function is the reciprocal function. We also show how logarithms can make complicated differentiation problems easier.
Lesson 14: Derivatives of Logarithmic and Exponential Functions (slides)Matthew Leingang
The exponential function is pretty much the only function whose derivative is itself. The derivative of the natural logarithm function is also beautiful as it fills in an important gap. Finally, the technique of logarithmic differentiation allows us to find derivatives without the product rule.
3.2 implicit equations and implicit differentiationmath265
The document discusses implicit equations and implicit differentiation. It begins by explaining the difference between explicit and implicit forms of equations, using the example of y=1/x which can be written explicitly as y=1/x or implicitly as xy=1. It then introduces the concept of implicit differentiation, which involves taking the derivative of an implicit equation with respect to x and solving for the derivative of y with respect to x (y’). This allows one to find the slope of the curve at a point, even if the explicit form of the relation between x and y is difficult to determine from the implicit equation.
Optimization Approach to Nash Euilibria with Applications to InterchangeabilityYosuke YASUDA
This document presents an optimization approach to characterizing Nash equilibria in games. It shows that the set of Nash equilibria is identical to the set of solutions that minimize an objective function defined over strategy profiles. This allows the equilibrium problem to be framed as an optimization problem. The approach provides a unified way to derive existing results on interchangeability of equilibria in zero-sum and supermodular games, by relating the properties of the objective function to the structure of the optimal solution set.
The document summarizes Fourier series and related concepts:
1. It defines periodic functions and their periods, gives examples like sin x and cos x having period 2π.
2. It states Dirichlet's conditions for a function to have a Fourier series representation and defines the Fourier coefficients using Euler's formulas.
3. It explains that the Fourier series of an even function contains only cosine terms and of an odd function contains only sine terms.
4. It provides examples of even and odd functions and solves problems finding Fourier series representations of specific functions.
1. The document summarizes key concepts in linear algebra including matrix operations, inverse matrices, determinants, eigenvalues and eigenvectors, and singular value decomposition.
2. Methods for matrix operations like row scaling, row addition, and row interchange are explained. Conditions for existence of inverse matrices involve the determinant being non-zero.
3. Eigenvalues and eigenvectors are introduced as solutions to Ax=λx. The eigendecomposition and singular value decomposition of matrices are summarized.
The document discusses linear transformations and linear independence. It contains examples and explanations of:
1) How a matrix A can transform a vector x from R4 to a new vector b in R2, representing the linear transformation.
2) How finding vectors x such that Ax=b is equivalent to finding pre-images of b under the transformation A.
3) Key concepts related to linear transformations like domain and range.
At times it is useful to consider a function whose derivative is a given function. We look at the general idea of reversing the differentiation process and its applications to rectilinear motion.
Lesson 16: Derivatives of Logarithmic and Exponential FunctionsMatthew Leingang
We show the the derivative of the exponential function is itself! And the derivative of the natural logarithm function is the reciprocal function. We also show how logarithms can make complicated differentiation problems easier.
Lesson 14: Derivatives of Logarithmic and Exponential Functions (slides)Matthew Leingang
The exponential function is pretty much the only function whose derivative is itself. The derivative of the natural logarithm function is also beautiful as it fills in an important gap. Finally, the technique of logarithmic differentiation allows us to find derivatives without the product rule.
3.2 implicit equations and implicit differentiationmath265
The document discusses implicit equations and implicit differentiation. It begins by explaining the difference between explicit and implicit forms of equations, using the example of y=1/x which can be written explicitly as y=1/x or implicitly as xy=1. It then introduces the concept of implicit differentiation, which involves taking the derivative of an implicit equation with respect to x and solving for the derivative of y with respect to x (y’). This allows one to find the slope of the curve at a point, even if the explicit form of the relation between x and y is difficult to determine from the implicit equation.
Optimization Approach to Nash Euilibria with Applications to InterchangeabilityYosuke YASUDA
This document presents an optimization approach to characterizing Nash equilibria in games. It shows that the set of Nash equilibria is identical to the set of solutions that minimize an objective function defined over strategy profiles. This allows the equilibrium problem to be framed as an optimization problem. The approach provides a unified way to derive existing results on interchangeability of equilibria in zero-sum and supermodular games, by relating the properties of the objective function to the structure of the optimal solution set.
The document summarizes Fourier series and related concepts:
1. It defines periodic functions and their periods, gives examples like sin x and cos x having period 2π.
2. It states Dirichlet's conditions for a function to have a Fourier series representation and defines the Fourier coefficients using Euler's formulas.
3. It explains that the Fourier series of an even function contains only cosine terms and of an odd function contains only sine terms.
4. It provides examples of even and odd functions and solves problems finding Fourier series representations of specific functions.
1. The document summarizes key concepts in linear algebra including matrix operations, inverse matrices, determinants, eigenvalues and eigenvectors, and singular value decomposition.
2. Methods for matrix operations like row scaling, row addition, and row interchange are explained. Conditions for existence of inverse matrices involve the determinant being non-zero.
3. Eigenvalues and eigenvectors are introduced as solutions to Ax=λx. The eigendecomposition and singular value decomposition of matrices are summarized.
The document discusses probability distributions and their natural parameters. It provides examples of several common distributions including the Bernoulli, multinomial, Gaussian, and gamma distributions. For each distribution, it derives the natural parameter representation and shows how to write the distribution in the form p(x|η) = h(x)g(η)exp{η^T μ(x)}. Maximum likelihood estimation for these distributions is also briefly discussed.
This document discusses machine learning and linear regression models. It covers the following topics:
1. What is machine learning and its purpose of generating algorithms to make predictions on unknown data based on known input data.
2. Linear regression models including definitions, parameters, and using the least squares method to predict parameters.
3. Non-linear regression models which use basis functions of x instead of x, and regularized methods to prevent overfitting.
Lesson 2: A Catalog of Essential Functions (slides)Matthew Leingang
This document provides an overview of different types of functions including: linear, polynomial, rational, power, trigonometric, and exponential functions. It discusses representing functions verbally, numerically, visually, and symbolically. Key topics covered include transformations of functions through shifting graphs vertically and horizontally, as well as composing multiple functions.
This document provides an overview of Math 5045: Advanced Algebra I (Module Theory). It begins with definitions of rings, including commutative and non-commutative rings, rings with unity, units, zero-divisors, integral domains, and fields. It then discusses modules, including left and right modules, submodules, module homomorphisms, kernels, images, and the module HomR(M,N). It also covers quotient modules and the composition of module homomorphisms.
This document discusses numerical integration techniques including the trapezoidal rule, Simpson's 1/3 rule, Simpson's 3/8 rule, and Gaussian integration formulas. It provides the formulas for calculating integration numerically using these methods and notes that accuracy increases with smaller interval widths h. Errors are estimated to be order h^2 for trapezoidal rule and order h^4 for Simpson's rules.
Continuous function have an important property that small changes in input do not produce large changes in output. The Intermediate Value Theorem shows that a continuous function takes all values between any two values. From this we know that your height and weight were once the same, and right now there are two points on opposite sides of the world with the same temperature!
Fourier series can be used to represent periodic and discontinuous functions. The document discusses:
1. The Fourier series expansion of a sawtooth wave, showing how additional terms improve the accuracy of the representation.
2. How Fourier series are well-suited to represent periodic functions over intervals like [0,2π] since the basis functions are also periodic.
3. An example of using Fourier series to analyze a square wave, finding the coefficients for its expansion in terms of sines and cosines.
Lecture slides on Decision Theory. The contents in large part come from the following excellent textbook.
Rubinstein, A. (2012). Lecture notes in microeconomic theory: the
economic agent, 2nd.
http://www.amazon.co.jp/dp/B0073X0J7Q/
Lesson 15: Exponential Growth and Decay (slides)Matthew Leingang
Many problems in nature are expressible in terms of a certain differential equation that has a solution in terms of exponential functions. We look at the equation in general and some fun applications, including radioactivity, cooling, and interest.
The document provides a review outline for Math 1a Midterm II covering topics including: differentiation using product, quotient, and chain rules; implicit differentiation; logarithmic differentiation; applications such as related rates and optimization; and the shape of curves including the mean value theorem and extreme value theorem. It also lists learning objectives and provides details on key concepts like L'Hopital's rule and the closed interval method for finding extrema.
The document discusses the difference quotient formula for calculating the slope of a cord connecting two points (x, f(x)) and (x+h, f(x+h)) on a function graph. It defines the difference quotient as (f(x+h) - f(x))/h, which calculates the slope as the change in y-values (f(x+h) - f(x)) over the change in x-values (h). An example calculates the slope of the cord connecting the points (2, f(2)) and (2.2, f(2.2)) on the function f(x) = x^2 - 2x + 2.
This document summarizes selection algorithms and the 1-center problem.
The selection algorithm uses a prune and search approach. It recursively partitions the dataset into subsets based on the median, pruning away elements that are guaranteed to be outside the desired rank. This results in a linear time complexity of O(n).
The 1-center problem finds the smallest circle enclosing a set of points. A constrained version restricts the center to a given line. The algorithm works by forming point pairs, computing bisectors, and recursively pruning points outside the optimal region.
By tracking the sign of distances to farthest points, the full 2D solution can also be obtained in linear time by recursively considering constrained subproblems on the x
There are three possible ROC's:
1. Outside all poles (a, b, c)
2. Between innermost and outermost pole
3. Inside all poles
So the possible ROC's are:
1. Outside circle through a, b, c
2. Annular region between a, c
3. Inside circle through a, b, c
a b c Re
The z-Transform
Important z-Transform Pairs
Important z-Transform Pairs
1. Unit Impulse: δ(n)
1, if n = 0
δ(n) = 0, otherwise
1
X(z) =
2.
This document discusses limit laws and theorems for computing limits. It provides 10 limit laws for operations like addition, subtraction, multiplication, division, powers, and roots. It also covers the direct substitution property, that limits can be found by substituting the point into the function if it is defined, and two important theorems: that limits can be compared if one function is always less than or equal to another, and the squeeze/sandwich theorem which allows limits to be found if a function is squeezed between two others with known limits. Examples are provided to illustrate finding limits through algebraic manipulation and using these theorems.
The document provides an overview of partitions and some of their applications. It begins with definitions of partitions and partition enumeration. Examples are given of partitions of small numbers. Several formulas are presented for counting partitions, including a generating function and an asymptotic formula. Applications discussed include symmetric functions, where partitions index polynomial bases, and representation theory of the symmetric group, where partitions label irreducible representations. Young diagrams and tableaux are introduced and used to define Schur functions.
This document discusses game theory and provides examples of different types of games. It introduces the prisoner's dilemma game, which involves two players who must choose whether to cooperate with or betray each other. It also discusses finding Nash equilibria, including examples of mixed strategy equilibria in the Battle of the Sexes and Matching Pennies games. The document provides information on concepts such as dominant strategies, Pareto optimality, best responses, and expected utility in game theory.
The document discusses the idea of integration by parts, which involves using the product rule in reverse to evaluate integrals that cannot be solved using other methods. It presents the integration by parts formula as u(x)v'(x)dx = u(x)v(x) - u'(x)v(x)dx and works through an example problem of evaluating the integral of xex dx using this formula. The example breaks the integral down into separate terms and shows that the overall integral equals xex - ex + C.
This chapter defines and investigates exponential and logarithmic functions. Exponential functions have a variable exponent and constant base, and are important due to their wide variety of applications including compound interest and radioactive decay. Logarithmic functions are defined as the inverse functions of exponential functions. The chapter explores properties of these functions such as their graphs and how to solve exponential and logarithmic equations. Objectives include defining the functions, investigating their properties, introducing applications, and solving related equations.
The document discusses functions and their properties. It defines a function as a rule that assigns exactly one output value to each input value in its domain. Functions can be represented graphically, numerically in a table, or with an algebraic rule. The domain of a function is the set of input values, while the range is the set of output values. Basic operations like addition, subtraction, multiplication and division can be performed on functions in the same way as real numbers. Composition of functions is defined as evaluating one function using the output of another as the input.
The document discusses probability distributions and their natural parameters. It provides examples of several common distributions including the Bernoulli, multinomial, Gaussian, and gamma distributions. For each distribution, it derives the natural parameter representation and shows how to write the distribution in the form p(x|η) = h(x)g(η)exp{η^T μ(x)}. Maximum likelihood estimation for these distributions is also briefly discussed.
This document discusses machine learning and linear regression models. It covers the following topics:
1. What is machine learning and its purpose of generating algorithms to make predictions on unknown data based on known input data.
2. Linear regression models including definitions, parameters, and using the least squares method to predict parameters.
3. Non-linear regression models which use basis functions of x instead of x, and regularized methods to prevent overfitting.
Lesson 2: A Catalog of Essential Functions (slides)Matthew Leingang
This document provides an overview of different types of functions including: linear, polynomial, rational, power, trigonometric, and exponential functions. It discusses representing functions verbally, numerically, visually, and symbolically. Key topics covered include transformations of functions through shifting graphs vertically and horizontally, as well as composing multiple functions.
This document provides an overview of Math 5045: Advanced Algebra I (Module Theory). It begins with definitions of rings, including commutative and non-commutative rings, rings with unity, units, zero-divisors, integral domains, and fields. It then discusses modules, including left and right modules, submodules, module homomorphisms, kernels, images, and the module HomR(M,N). It also covers quotient modules and the composition of module homomorphisms.
This document discusses numerical integration techniques including the trapezoidal rule, Simpson's 1/3 rule, Simpson's 3/8 rule, and Gaussian integration formulas. It provides the formulas for calculating integration numerically using these methods and notes that accuracy increases with smaller interval widths h. Errors are estimated to be order h^2 for trapezoidal rule and order h^4 for Simpson's rules.
Continuous function have an important property that small changes in input do not produce large changes in output. The Intermediate Value Theorem shows that a continuous function takes all values between any two values. From this we know that your height and weight were once the same, and right now there are two points on opposite sides of the world with the same temperature!
Fourier series can be used to represent periodic and discontinuous functions. The document discusses:
1. The Fourier series expansion of a sawtooth wave, showing how additional terms improve the accuracy of the representation.
2. How Fourier series are well-suited to represent periodic functions over intervals like [0,2π] since the basis functions are also periodic.
3. An example of using Fourier series to analyze a square wave, finding the coefficients for its expansion in terms of sines and cosines.
Lecture slides on Decision Theory. The contents in large part come from the following excellent textbook.
Rubinstein, A. (2012). Lecture notes in microeconomic theory: the
economic agent, 2nd.
http://www.amazon.co.jp/dp/B0073X0J7Q/
Lesson 15: Exponential Growth and Decay (slides)Matthew Leingang
Many problems in nature are expressible in terms of a certain differential equation that has a solution in terms of exponential functions. We look at the equation in general and some fun applications, including radioactivity, cooling, and interest.
The document provides a review outline for Math 1a Midterm II covering topics including: differentiation using product, quotient, and chain rules; implicit differentiation; logarithmic differentiation; applications such as related rates and optimization; and the shape of curves including the mean value theorem and extreme value theorem. It also lists learning objectives and provides details on key concepts like L'Hopital's rule and the closed interval method for finding extrema.
The document discusses the difference quotient formula for calculating the slope of a cord connecting two points (x, f(x)) and (x+h, f(x+h)) on a function graph. It defines the difference quotient as (f(x+h) - f(x))/h, which calculates the slope as the change in y-values (f(x+h) - f(x)) over the change in x-values (h). An example calculates the slope of the cord connecting the points (2, f(2)) and (2.2, f(2.2)) on the function f(x) = x^2 - 2x + 2.
This document summarizes selection algorithms and the 1-center problem.
The selection algorithm uses a prune and search approach. It recursively partitions the dataset into subsets based on the median, pruning away elements that are guaranteed to be outside the desired rank. This results in a linear time complexity of O(n).
The 1-center problem finds the smallest circle enclosing a set of points. A constrained version restricts the center to a given line. The algorithm works by forming point pairs, computing bisectors, and recursively pruning points outside the optimal region.
By tracking the sign of distances to farthest points, the full 2D solution can also be obtained in linear time by recursively considering constrained subproblems on the x
There are three possible ROC's:
1. Outside all poles (a, b, c)
2. Between innermost and outermost pole
3. Inside all poles
So the possible ROC's are:
1. Outside circle through a, b, c
2. Annular region between a, c
3. Inside circle through a, b, c
a b c Re
The z-Transform
Important z-Transform Pairs
Important z-Transform Pairs
1. Unit Impulse: δ(n)
1, if n = 0
δ(n) = 0, otherwise
1
X(z) =
2.
This document discusses limit laws and theorems for computing limits. It provides 10 limit laws for operations like addition, subtraction, multiplication, division, powers, and roots. It also covers the direct substitution property, that limits can be found by substituting the point into the function if it is defined, and two important theorems: that limits can be compared if one function is always less than or equal to another, and the squeeze/sandwich theorem which allows limits to be found if a function is squeezed between two others with known limits. Examples are provided to illustrate finding limits through algebraic manipulation and using these theorems.
The document provides an overview of partitions and some of their applications. It begins with definitions of partitions and partition enumeration. Examples are given of partitions of small numbers. Several formulas are presented for counting partitions, including a generating function and an asymptotic formula. Applications discussed include symmetric functions, where partitions index polynomial bases, and representation theory of the symmetric group, where partitions label irreducible representations. Young diagrams and tableaux are introduced and used to define Schur functions.
This document discusses game theory and provides examples of different types of games. It introduces the prisoner's dilemma game, which involves two players who must choose whether to cooperate with or betray each other. It also discusses finding Nash equilibria, including examples of mixed strategy equilibria in the Battle of the Sexes and Matching Pennies games. The document provides information on concepts such as dominant strategies, Pareto optimality, best responses, and expected utility in game theory.
The document discusses the idea of integration by parts, which involves using the product rule in reverse to evaluate integrals that cannot be solved using other methods. It presents the integration by parts formula as u(x)v'(x)dx = u(x)v(x) - u'(x)v(x)dx and works through an example problem of evaluating the integral of xex dx using this formula. The example breaks the integral down into separate terms and shows that the overall integral equals xex - ex + C.
This chapter defines and investigates exponential and logarithmic functions. Exponential functions have a variable exponent and constant base, and are important due to their wide variety of applications including compound interest and radioactive decay. Logarithmic functions are defined as the inverse functions of exponential functions. The chapter explores properties of these functions such as their graphs and how to solve exponential and logarithmic equations. Objectives include defining the functions, investigating their properties, introducing applications, and solving related equations.
The document discusses functions and their properties. It defines a function as a rule that assigns exactly one output value to each input value in its domain. Functions can be represented graphically, numerically in a table, or with an algebraic rule. The domain of a function is the set of input values, while the range is the set of output values. Basic operations like addition, subtraction, multiplication and division can be performed on functions in the same way as real numbers. Composition of functions is defined as evaluating one function using the output of another as the input.
This document provides an overview of exponential and logarithmic functions. It defines exponential functions as functions of the form f(x) = A*bx, where A and b are constants. Exponential functions model phenomena like radioactive decay and population growth. Logarithms are defined as the power to which a base number must be raised to equal the input value. Properties of logarithms include log(ab) = log(a) + log(b). Logarithms are useful for solving equations involving exponents. The derivatives of logarithmic and exponential functions are discussed but not derived until integration is covered.
Exponential and logarithm functions are important in both theory and practice. They examine the graphs of exponential functions f(x)=ax where a>0 and logarithm functions f(x)=loga(x) where a>0. It is important to practice these functions so their properties become intuitive. Key properties include exponential functions where a>1 increase rapidly for positive x and 0<a<1 increase for decreasing negative x, and both pass through (0,1). The natural logarithm function f(x)=ln(x) is particularly important.
32 Ways a Digital Marketing Consultant Can Help Grow Your BusinessBarry Feldman
How can a digital marketing consultant help your business? In this resource we'll count the ways. 24 additional marketing resources are bundled for free.
Lesson 14: Derivatives of Logarithmic and Exponential Functions (slides)Mel Anthony Pepito
This document provides an overview of the key points covered in a calculus lecture on derivatives of logarithmic and exponential functions:
1) It discusses the derivatives of exponential functions with any base, as well as the derivatives of logarithmic functions with any base.
2) It covers using the technique of logarithmic differentiation to find derivatives of functions involving products, quotients, and/or exponentials.
3) The document provides examples of finding derivatives of various logarithmic and exponential functions.
Graphs of the Sine and Cosine Functions LectureFroyd Wess
More: www.PinoyBIX.org
Lesson Objectives
Able to plot the different Trigonometric Graphs
Graph of Sine Function (y = f(x) = sinx)
Graph of Cosine Function (y = f(x) = cosx)
Define the Maximum and Minimum value in a graph
Generalized Trigonometric Functions
Graphs of y = sinbx
Graphs of y = sin(bx + c)
Could find the Period of Trigonometric Functions
Could find the Amplitude of Trigonometric Functions
Variations in the Trigonometric Functions
The document discusses continuity of functions and graphs. It defines a continuous function as one where the graph is unbroken within its domain. A function is discontinuous if its graph is broken. Continuity at a point x=a can be determined by comparing the left and right limits of the function at a to the actual value of the function at a. If the limits are equal to the function value, it is continuous from that side. The document provides examples of functions that are right continuous, left continuous, or discontinuous at various points to illustrate these concepts.
A function is a rule that assigns exactly one output to each input. An example of something that is not a function is a rule that assigns two outputs to some inputs. The vertical line test can be used to determine if a relationship represents a function - if a vertical line intersects the graph at more than one point, it is not a function. Polynomial functions have the general form f(x) = Axn + Bxn-1 + ... + Tx + U. To sketch the graph of a polynomial function, one finds the x-intercepts by setting the function equal to 0 and solving for x, determines the end behavior by looking at the highest power term, finds the multiplicity of each x-intercept, finds
The document discusses transformations of parent functions through various parameters. It introduces common parent functions and explains how changing parameters like a, h, and k transform the functions in different ways. The a parameter vertically stretches or shrinks functions, h shifts functions left or right along the x-axis, and k shifts functions up or down along the y-axis. Less common transformations include reflecting over the y-axis or horizontally stretching and shrinking using other parameters. Examples are provided to demonstrate combining multiple transformations on functions.
This document discusses exponential functions of the form y = bx and how their graphs are affected by changes in the value of b. It introduces translations of exponential functions of the form y = b(x - h) + k, explaining that a positive k shifts the graph up, a negative k shifts it down, a positive h shifts it left, and a negative h shifts it right. Examples are presented to illustrate these effects on the graphs of translating the function y = 2x.
This document discusses polynomial functions. It defines a polynomial as a function involving only non-negative integer powers of x, and states that the degree of a polynomial is the highest power of x in its expression. It examines the graphs of polynomials up to degree 4 and how changing coefficients affects the graph. It also discusses turning points, roots, and multiplicity of roots of polynomials.
This document discusses algebraic functions, including polynomial and rational functions. Polynomial functions are functions of the form y = p(x) = a0 + a1x + a2x2 + ... + anxn, where ai ∈ R and an ≠ 0. Rational functions are functions of the form y = R(x) = P(x)/Q(x), where P(x) and Q(x) are polynomial functions. The document outlines how to analyze the domain, intercepts, symmetries, asymptotes, and graph of algebraic functions. It provides examples of discussing these "aids to graphing" and sketching the graphs of specific rational functions.
The document discusses functions and their derivatives. It defines functions, different types of functions, and notation used for functions. It then covers the concept of limits, theorems on limits, and limits at infinity. The document defines the slope of a tangent line to a curve and increments. It provides definitions and rules for derivatives, including differentiation from first principles and various differentiation rules. It includes examples of finding derivatives using these rules and taking multiple derivatives.
1) The document discusses various methods for graphing polynomials, including using function shift rules to graph even and odd powers, using the leading term test to predict behavior, and graphing using known zeros and the multiplicity rules.
2) The multiplicity rules state that a zero with even multiplicity will cause the graph to "bounce off" the x-intercept, while an odd multiplicity will cause the graph to pass through the intercept.
3) An example graphs a polynomial by factoring it, finding the zeros, and applying the multiplicity rules to the graph.
This document provides an overview of various types of functions and their graphs. It begins with linear functions of the form y=mx+c and discusses how shifting these functions along the x- or y-axis changes their graphs. It then covers quadratic, square root, cube, reciprocal, constant, identity and absolute value functions. Piecewise, polynomial, algebraic, and transcendental functions are also defined. The document discusses bounded vs unbounded functions and concludes by examining circular and hyperbolic functions and their graphs.
A polynomial function is a function where all exponents are non-negative integers. It is of the form f(x) = anxn + an-1xn-1 + ... + a1x + a0, where n is a non-negative integer and the coefficients ai are real numbers. The degree of a polynomial is the highest exponent present, and the leading coefficient is the coefficient of the term with the highest degree. The end behavior of a polynomial function can be determined by looking at the sign of the leading coefficient and whether the highest degree is even or odd. A polynomial function has at most n real zeros, where n is its degree, and at most n-1 turning points.
The document discusses factorable polynomials and graphing them. It defines a factorable polynomial P(x) as one that can be written as the product of linear factors P(x) = an(x - r1)(x - r2)...(x - rk), where r1, r2, etc. are the roots of P(x). It explains that for large values of |x|, the leading term of P(x) dominates so the graph resembles that of the leading term, while near the roots other terms contribute to the shape of the graph. Examples of graphs of polynomials like x^n are provided to illustrate the approach.
The document provides an overview of Calculus I taught by Professor Matthew Leingang at New York University. It outlines key topics that will be covered in the course, including different classes of functions, transformations of functions, and compositions of functions. The first assignments are due on January 31 and February 2, with first recitations on February 3. The document uses examples to illustrate concepts like linear functions, other polynomial functions, and trigonometric functions. It also explains how vertical and horizontal shifts can transform the graph of a function.
The document discusses graphs of rational functions and how to analyze them. It covers identifying vertical and horizontal asymptotes analytically by finding values where the denominator is zero or analyzing behavior as x approaches positive/negative infinity. Examples show graphing points around asymptotes and confirming trends to sketch the graph. Key steps are finding intercepts and asymptotes, making a T-chart of values, and graphing with curves between points and asymptotes. Practice problems are provided to apply these skills.
The document discusses graphs of rational functions and how to analyze them. It covers identifying vertical and horizontal asymptotes analytically by finding values where the denominator is zero or analyzing behavior as x approaches positive/negative infinity. Examples show graphing rational functions by plotting points near asymptotes and connecting them, confirming asymptotes numerically. Key steps are finding intercepts and asymptotes, making a table near asymptotes, and graphing points and asymptotes. The reader is assigned practice problems applying these skills.
- Derivatives describe how a quantity is changing with respect to something else, like how velocity changes over time.
- The derivative of a function y(x) at a point x is the slope of the tangent line to the curve of y(x) at that point.
- Mathematically, the derivative dy/dx is defined as the limit as h approaches 0 of the change in y over the change in x, (y(x+h)-y(x))/h.
- For functions of the form y(x)=Ax^n, the derivative has a shortcut of dy/dx=nAx^(n-1).
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
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Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
1050 text-ef
1. Exponential Functions
In this chapter, a will always be a positive number.
For any positive number a > 0, there is a function f : R → (0, ∞) called
an exponential function that is defined as f (x) = ax .
4
For example, f (x) = 3x is an exponential function, and g(x) = ( 17 )x is an
exponential function.
There is a big difference between an exponential function and a polynomial.
The function p(x) = x3 is a polynomial. Here the “variable”, x, is being raised
to some constant power. The function f (x) = 3x is an exponential function;
the variable is the exponent.
Rules for exponential functions
Here are some algebra rules for exponential functions that will be explained
in class.
If n ∈ N, then an is the product of n a’s. For example, 34 = 3 · 3 · 3 · 3 = 81
a0 = 1
If n, m ∈ N, then
n √ √
am = m
an = ( m a)n
1
a−x =
ax
The rules above were designed so that the following most important rule
of exponential functions holds:
149
2. ax ay = ax+y
Another variant of the important rule above is
ax
y
= ax−y
a
And there is also the following slightly related rule
(ax )y = axy
Examples.
1 √
2
• 42 = 4=2
• 7−2 · 76 · 7−4 = 7−2+6−4 = 70 = 1
1 1
• 10−3 = 103 = 1000
156
• 155 = 156−5 = 151 = 15
• (25 )2 = 210 = 1024
1
• (320 ) 10 = 32 = 9
2 1 1 1 1
• 8− 3 = 2 = √
3 2 = 22
= 4
(8) 3 ( 8)
* * * * * * * * * * * * *
150
3. The base of an exponential function
If f (x) = ax , then we call a the base of the exponential function. The base
must always be positive.
Base 1
If f (x) is an exponential function whose base equals 1 – that is if f (x) = 1x
– then for n, m ∈ N we have
n n √ √
m m
f = 1 m = 1n = 1 = 1
m
In fact, for any real number x, 1x = 1, so f (x) = 1x is the same function as
the constant function f (x) = 1. For this reason, we usually don’t talk much
about the exponential function whose base equals 1.
* * * * * * * * * * * * *
Graphs of exponential functions
It’s really important that you know the general shape of the graph of an
exponential function. There are two options: either the base is greater than
1, or the base is less than 1 (but still positive).
Base greater than 1. If a is greater than 1, then the graph of f (x) = ax
grows taller as it moves to the right. To see this, let n ∈ Z. We know
that 1 a, and we know from our rules of inequalities that we can multiply
both sides of this inequality by a positive number. The positive number we’ll
multiply by is an , so that we’ll have
an (1) an a
Because an (1) = an and an a = an+1 , the inequality above is the same as
an an+1
Because the last inequality we found is true for any n ∈ Z, we actually have
an entire string of inequalities:
· · · a−3 a−2 a−1 a0 a1 a2 a3 · · ·
Keeping in mind that ax is positive for any number x, and that a0 = 1,
we now have a pretty good idea of what the graph of f (x) = ax looks like if
a 1. The y-intercept is at 1; when moving to the right, the graph grows
151
4. taller and taller; and when moving toto the left, the graph becomes shorter and
taller and taller; and when moving the left, the graph becomes shorter and
taller and taller; and when moving to the left, the graph becomes shorter and
shorter, shrinking towards, but never touching, the x-axis.
shorter, shrinking towards, but never touching, the x-axis.
shorter, shrinking towards, but never touching, the x-axis.
Not only does the graph grow bigger as it moves to the right, but it gets
big in only does For example, if we lookas itthe exponential function it gets
Not a hurry. the graph grow bigger at moves to the right, but whose
big Not2, then
only does the graph grow bigger as it moves to the right, but it gets
basein a hurry. For example, if we look at the exponential function whose
is
big in a hurry. For example, if we look at the exponential function whose
base is 2, then 64
base is 2, then f (64) = 2 64 = 18, 446, 744, 073, 709, 525, 000
f (64) = 2 64 18, 446, 744, 073, 709, 525, 000
=
And 2 isn’t even f (64) =big number to744, 073, 709, 525,base (any positive
a very 2 = 18, 446, be using for a 000
number isn’t be a base, and plenty of numbers are much, base (any positive
And 2 can even a very big number to be using for a much bigger than
number can be a base andbig number to be using for a much (any positive
And 2 isn’t even a very base bigger than
2). The bigger the base, of anplenty of numbers are the faster its graph grows
exponential function, much,
2). The biggerbe a base and plenty of numbers are much, much bigger than 2).
number can the base
as it moves to the right.of an exponential function, the faster its graph grows
asMoving to to the right. graph of f (x) = function, small very it grows. a 1.
Themoves the base of an exponential x
it biggerthe left, the a grows
x
the faster
quickly if
Moving tolook left, theexponential(x) == x grows small very quickly if if a 1.
Moving to the left, the graph of f (x) a a grows small very quickly a 1.
Again if we the at the graph of f function whose base is 2, then
Again if if we look at the exponential function whose base is 2, then
Again we look at the exponential function whose base is 2, then
1 1
f (−10) = 2−10 = 1 1= 1 1
−10
f (−10) == −10 == 1010 = 1024
f (−10) 2 2 210 = 1024
2
The bigger the base, the faster the graph 2 an exponential function shrinks
of 1024
The bigger the base, the faster the graph of an exponential function shrinks
as it bigger thethe left. faster the graph of an exponential function shrinks
The moves to base, the
as itwe move to the left.
as moves to the left. 152
4
145
5. Base less than 1 1(but still positive).
Base less than 1 (but still positive). If a is positive and less than 1,
If a is positive and less than 1,
then weless can show fromstill positive).
Base we than (but our rules of inequalities that n+1 and an for any1,
If a is positive
inequalities that a
n+1 less than
then we we can show from our rules of of inequalities thata an+1 nafor any
a n for any
nthen we we means thatfrom our rules
∈Z. That can show
Z.
n ∈ ∈ Z.That means that
n That means−3 that
a a−2 a−1 a a a a
····· a−3 a−2 a−1 a00 a11 a22 a33 ······
· · · · a−3 a−2 a−1 a0 a1 a2 a3 · · ·
So the graph of f (x) = a x when the base is smaller than 1 slopes down as
the graph of f a smaller than 1 slopes down as
SoSo the graph of (x) = =xawhen the base is is smaller than 1 slopes down as
x
f (x) when the base
it it moves to the right, but it is always positive.As it it moves to the left, the
it moves to the right, but it is always positive. As it moves to the left, the
moves to the right, but it is always positive.
As
moves to the left, the
graph grows tall very quickly.
graph grows tall very quickly.
graph grows tall very quickly.
One-to-one and onto
One-to-one and onto
One-to-one exponential function f R → (0, ∞) has as its domain the
Recall that an and onto
Recall that an exponential function f :: R → (0, ∞) has as its domain the
set Recall that as its target the function f : R → (0, ∞) has as its domain the
R and has an exponential set (0, ∞).
set R and has as its target the set (0, ∞).
set R and hasthe graph of fthe seta(0, ∞).
We see from as its target (x) = x if either a 1 or 0 a 1, that f (x)
We see from the graph of f (x) = ax ,,xifeither a 1 or 0 a 1, that f (x)
is one-to-one and onto. Remember a , if to check 1for 0 is a 1, that f (x)
We see from the graph of f (x) = that either a if (x) one-to-one, we
is one-to-one and onto. Remember that to check if f (x) is one-to-one, we
can one-to-one and onto. Remember that passes). To f (x) is one-to-one, we
is use the horizontal line test (which f (x) to check if check what the range
can use the horizontal line test (which f (x) passes). To check what the range
ofcan use the horizontalcompressing the f (x) passes). To check what theIf we
f (x) is, we think of line test (which graph of f (x) onto the y-axis. range
of f (x) is, we think of compressing the graph of f (x) onto the y-axis. If we
did that, is, we think ofthat the rangethe f (x) is of f (x) onto the y-axis. If we
of f (x) we would see compressing of graph the set of positive numbers,
did that, we would see that the range of f (x) is the set of positive numbers,
(0, ∞). Since the range and target of f (x) fare thethe setset,positive onto.
did that, we would see that the range of (x) is same of f (x) is numbers,
(0, ∞). Since the range and target of 146(x) are the same set, f (x) is onto.
f
(0, ∞). Since the range and target 153 f (x) are the same set, f (x) is onto.
of
5
6. * * * * * * * * * * * * *
Where exponential functions appear
Exponential functions are closely related to geometric sequences. They
appear whenever you are multiplying by the same number over and over and
over again.
The most common example is in population growth. If a population of a
group increases by say 5% every year, then every year the total population
is multiplied by 105%. That is, after one year the population is 1.05 times
what it originally was. After the second year, the population will be (1.05)2
times what it originally was. After 100 years, the population will be (1.05)100
times what it originally was. After x years, the population will be (1.05)x
times what it originally was.
Interest rates on credit cards measure a population growth of sorts. If your
credit card charges you 20% interest every year, then after 5 years of not
making payments, you will owe (1.20)5 = 2.48832 times what you originally
charged on your credit card. After x years of not making payments, you will
owe (1.20)x times what you originally charged.
Sometimes a quantity decreases exponentially over time. This process is
called exponential decay.
If a tree dies to become wood, the amount of carbon in it decreases by
0.0121% every year. Scientists measure how much carbon is in something
that died, and use the exponential function f (x) = (0.999879)x to figure out
when it must have died. (The number 0.999879 is the base of this exponential
function because 0.999879 = 1 − 0.000121.) This technique is called carbon
dating and it can tell us about history. For example, if scientists discover
that the wood used to build a fort came from trees that died 600 years ago,
then the fort was probably built 600 years ago.
* * * * * * * * * * * * *
e
Some numbers are so important in math that they get their own name.
One such number is e. It is a real number, but it is not a rational number.
It’s very near to – but not equal to – the rational number 27 = 2.7. The
10
154
7. importance of the number e becomes more apparent after studying calculus,
but it can be explained without calculus.
Let’s say you just bought a new car. You’re driving it off the lot, and the
odometer says that it’s been driven exactly 1 mile. You are pulling out of the
lot slowly at 1 mile per hour, and for fun you decide to keep the odometer
and the speedometer so that they always read the same number.
After something like an hour, you’ve driven one mile, and the odometer
says 2, so you accelerate to 2 miles per hour. After driving for something like
a half hour, the odometer says 3, so you speed up to 3 miles an hour. And
you continue in this fashion.
After some amount of time, you’ve driven 100 miles, so you are moving at
a speed of 100 miles per hour. The odometer will say 101 after a little while,
and then you’ll have to speed up. After you’ve driven 1000 miles (and here’s
where the story starts to slide away from reality) you’ll have to speed up to
1000 miles per hour. Now it will be just around 3 seconds before you have
to speed up to 1001 miles per hour.
You’re traveling faster and faster, and as you travel faster, it makes you
travel faster, which makes you travel faster still, and things get out of hand
very quickly, even though you started out driving at a very reasonable speed
of 1 mile per hour.
If x is the number of hours you had been driving for, and f (x) was the
distance the car had travelled at time x, then f (x) is the exponential function
with base e. In symbols, f (x) = ex .
Calculus studies the relationship between a function and the slope of the
graph of the function. In the previous example, the function was distance
travelled, and the slope of the distance travelled is the speed the car is moving
at. The exponential function f (x) = ex has at every number x the same
“slope” as the value of f (x). That makes it a very important function for
calculus.
For example, at x = 0, the slope of f (x) = ex is f (0) = e0 = 1. That
means when you first drove off the lot (x = 0) the odometer read 1 mile, and
your speed was 1 mile per hour. After 10 hours of driving, the car will have
travelled e10 miles, and you will be moving at a speed of e10 miles per hour.
(By the way, e10 is about 22, 003.)
* * * * * * * * * * * * *
155
8. Exercises
For #1-11, write each number in simplest form without using a calculator,
as was done in the “Examples” in this chapter. (On exams you will be asked
to simplify problems like these without a calculator.)
1.) 8−1
2.) ( 17 )3 ( 17 )−3
43 43
1
3.) 125− 3
4.) 100−5 · 100457 · 100−50 · 100−400
3
5.) 4− 2
1
6.) (3200 ) 100
2
7.) 1000 3
8.) 97−16 9715
3
9.) 36 6
3297
10.) 3300
4 14
11.) (5 7 ) 4
12.) Graph f (x) = 100( 1 )x
2
13.) Graph f (x) = 3(x−4) + 7
14.) Graph f (x) = −2(x+5)
4
15.) Graph f (x) = ( 17 )−x − 10
156
9. For #16-24, decide which is the only number x that satisfies the given
equation.
16.) 4x = 16
17.) 2x = 8
18.) 10x = 10, 000
19.) 3x = 9
20.) 5x = 125
21.) ( 1 )x = 16
2
22.) ( 1 )x = 64
4
1
23.) 8x = 4
1
24.) 27x = 9
25.) Suppose you accidentally open a canister of plutonium in your living
room and 160 units of radiation leaks out. If every year, there is half as much
radiation as there was the year before, will your living room ever be free of
radiation? How many units of radiation will there be after 4 years?
26.) Your uncle has an investment scheme. He guarantees that if you invest
in the stock of his company, then you’ll earn 10% on your money every year.
If you invest $100, and you uncle is right, how much money will you have
after 20 years?
157