Infomatica, as it stands today, is a manifestation of our values, toil, and dedication towards imparting knowledge to the pupils of the society. Visit us: http://www.infomaticaacademy.com/
- Euler's theorem states that for a homogeneous function f(x) of degree k, the partial derivative of f with respect to x is equal to kf(x)/x.
- Homogeneous functions have special properties related to their degree of homogeneity. One important property is described by Euler's theorem.
- National income can be modeled as a homogeneous function of degree one, implying some key relationships between its arguments.
This document discusses various methods for solving first order differential equations, including:
1. Variable separable methods where the equation can be written as a function of x multiplied by a function of y.
2. Homogeneous equations where both sides are homogeneous functions of the same degree.
3. Exact equations where there exists an integrating factor.
4. Equations that can be transformed to an exact or separable form through substitution.
5. Linear equations that can be solved using an integrating factor that is a function of x.
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.
- A differential equation involves an independent variable, dependent variable, and derivatives of the dependent variable with respect to the independent variable.
- The order of a differential equation is the order of the highest derivative, and the degree is the exponent of the highest order derivative.
- Linear differential equations involve the dependent variable and its derivatives only to the first power. Non-linear equations do not meet this criterion.
- The general solution of a differential equation contains as many arbitrary constants as the order of the equation. A particular solution results from assigning values to the arbitrary constants.
- Differential equations can be solved through methods like variable separation, inspection of reducible forms, and finding homogeneous or linear representations.
The document discusses evaluating the formula log[(2x+1)/(sin1/3(x)+1)] at x=0 and x=10 degrees using a scientific calculator. It explains that the answer is 0 at x=0 and approximately 1.13 at x=10 degrees. It then describes the keyboard of a typical scientific calculator, noting the number, operation, yx, sin, log and formula keys. The rest of the document provides examples and definitions of algebraic, trigonometric and exponential-log formulas.
This document discusses differentiation and derivatives. It defines differentiation as finding the average rate of change of one variable with respect to another. It then discusses various methods of finding derivatives, including the direct method using derivative rules, as well as discussing specific rules like the power rule, product rule, quotient rule, chain rule, and rules for derivatives of trigonometric, exponential, and logarithmic functions.
This document provides an overview of vector spaces and related concepts such as linear combinations, spans, bases, and subspaces. Some key points:
- A vector space is a set equipped with vector addition and scalar multiplication satisfying certain properties. Examples include Rm and the space of polynomials.
- A linear combination of vectors is a sum of the form v = x1v1 + x2v2 + ... + xnvn. The span of vectors is the set of all their linear combinations.
- A set of vectors is linearly independent if the only way to get the zero vector as a linear combination is with all scalars equal to zero.
- A basis is a linearly independent set of vectors
The document discusses concepts related to partial differentiation and its applications. It covers topics like tangent planes, linear approximations, differentials, Taylor expansions, maxima and minima problems, and the Lagrange method. Specifically, it defines the tangent plane to a surface at a point using partial derivatives, describes how to find the linear approximation of functions, and explains how to find maximum and minimum values of functions using critical points and the second derivative test.
- Euler's theorem states that for a homogeneous function f(x) of degree k, the partial derivative of f with respect to x is equal to kf(x)/x.
- Homogeneous functions have special properties related to their degree of homogeneity. One important property is described by Euler's theorem.
- National income can be modeled as a homogeneous function of degree one, implying some key relationships between its arguments.
This document discusses various methods for solving first order differential equations, including:
1. Variable separable methods where the equation can be written as a function of x multiplied by a function of y.
2. Homogeneous equations where both sides are homogeneous functions of the same degree.
3. Exact equations where there exists an integrating factor.
4. Equations that can be transformed to an exact or separable form through substitution.
5. Linear equations that can be solved using an integrating factor that is a function of x.
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.
- A differential equation involves an independent variable, dependent variable, and derivatives of the dependent variable with respect to the independent variable.
- The order of a differential equation is the order of the highest derivative, and the degree is the exponent of the highest order derivative.
- Linear differential equations involve the dependent variable and its derivatives only to the first power. Non-linear equations do not meet this criterion.
- The general solution of a differential equation contains as many arbitrary constants as the order of the equation. A particular solution results from assigning values to the arbitrary constants.
- Differential equations can be solved through methods like variable separation, inspection of reducible forms, and finding homogeneous or linear representations.
The document discusses evaluating the formula log[(2x+1)/(sin1/3(x)+1)] at x=0 and x=10 degrees using a scientific calculator. It explains that the answer is 0 at x=0 and approximately 1.13 at x=10 degrees. It then describes the keyboard of a typical scientific calculator, noting the number, operation, yx, sin, log and formula keys. The rest of the document provides examples and definitions of algebraic, trigonometric and exponential-log formulas.
This document discusses differentiation and derivatives. It defines differentiation as finding the average rate of change of one variable with respect to another. It then discusses various methods of finding derivatives, including the direct method using derivative rules, as well as discussing specific rules like the power rule, product rule, quotient rule, chain rule, and rules for derivatives of trigonometric, exponential, and logarithmic functions.
This document provides an overview of vector spaces and related concepts such as linear combinations, spans, bases, and subspaces. Some key points:
- A vector space is a set equipped with vector addition and scalar multiplication satisfying certain properties. Examples include Rm and the space of polynomials.
- A linear combination of vectors is a sum of the form v = x1v1 + x2v2 + ... + xnvn. The span of vectors is the set of all their linear combinations.
- A set of vectors is linearly independent if the only way to get the zero vector as a linear combination is with all scalars equal to zero.
- A basis is a linearly independent set of vectors
The document discusses concepts related to partial differentiation and its applications. It covers topics like tangent planes, linear approximations, differentials, Taylor expansions, maxima and minima problems, and the Lagrange method. Specifically, it defines the tangent plane to a surface at a point using partial derivatives, describes how to find the linear approximation of functions, and explains how to find maximum and minimum values of functions using critical points and the second derivative test.
The document discusses partial differentiation and its applications. It covers functions of two variables, first and second partial derivatives, and applications including the Cobb-Douglas production function and finding marginal productivity from a production function. Examples are provided to demonstrate calculating partial derivatives of various functions and applying partial derivatives in contexts like production analysis.
The document discusses derivatives and graphs. It defines interval notation used to indicate whether points are included or excluded from intervals. It then explains that the derivative of a function f(x) at a point x, f'(x), represents the slope of the tangent line to the graph of f(x) at (x, f(x)). Finally, it notes that points where the derivative is 0 are called critical points, as the tangent line is flat at these points.
The document discusses interpolation, which involves using a function to approximate values between known data points. It provides examples of Lagrange interpolation, which finds a polynomial passing through all data points, and Newton's interpolation, which uses divided differences to determine coefficients for approximating between points. The examples demonstrate constructing Lagrange and Newton interpolation polynomials using given data sets.
This document summarizes the Runge-Kutta methods for solving differential equations numerically. It introduces the first, second, third, and fourth order Runge-Kutta methods and provides the equations for calculating each. An example of using the fourth order Runge-Kutta method to solve the differential equation dy/dx=x+y is shown step-by-step. The example calculates the solution to y(0.2) given y(0)=1 using increments of h=0.1.
The document provides examples to illustrate how to find the eigenvalues and eigenvectors of a matrix.
1) For a 2x2 matrix, the characteristic polynomial is computed by taking the determinant of the matrix minus the identity matrix. The roots of the characteristic polynomial are the eigenvalues. The corresponding eigenvectors are found by solving the original eigenvalue equation.
2) For a triangular matrix, the eigenvalues are the diagonal elements. The eigenvectors are found by setting rows corresponding to non-diagonal elements to zero.
3) The document provides a numerical example to demonstrate finding the eigenvalues (3, 1, -2) and eigenvectors of a 3x3 matrix.
In mathematics, a partial derivative of a function of several variables is its derivative with respect to one of those variables, with the others held constant (as opposed to the total derivative, in which all variables are allowed to vary). Partial derivatives are used in vector calculus and differential geometry.
The document discusses integration by parts and the tableau method for evaluating integrals. It provides the formula for integration by parts, an example using the tableau method to evaluate the integral of (x3 + 2x)ex/2dx, and discusses how the tableau method can be used to evaluate integrals involving polynomials and functions that can be integrated repeatedly, such as ex, sin(x), and cos(x). It also provides the formulas for antiderivatives of trigonometric powers like cosn(x).
The document discusses several key concepts regarding derivatives:
(1) It explains how to use the derivative to determine if a function is increasing, decreasing, or neither on an interval using the signs of the derivative.
(2) It provides theorems and rules for finding local extrema (maxima and minima) of functions using the first and second derivative tests.
(3) It also discusses absolute extrema, monotonic functions, and the Rolle's Theorem and Mean Value Theorem which relate the derivative of a function to values of the function.
The document is notes for a lesson on partial derivatives. It introduces partial derivatives and their motivation as slopes of curves through a point on a multi-variable function. It defines partial derivatives mathematically and gives an example. It also discusses second partial derivatives and notes that mixed partials are always equal due to Clairaut's Theorem when the function is continuous. Finally, it provides an example of calculating second partial derivatives.
The document discusses vector spaces and subspaces. It defines vectors in Rn as n-tuples of real numbers and describes operations on vectors like addition and scalar multiplication. A vector space is a set with vectors that is closed under these operations and satisfies other axioms. Examples given include Rn, the space of matrices, and polynomial spaces. A subspace is a subset of a vector space that itself is a vector space under the operations in the larger space.
Linear differential equation with constant coefficientSanjay Singh
The document discusses linear differential equations with constant coefficients. It defines the order, auxiliary equation, complementary function, particular integral and general solution. It provides examples of determining the complementary function and particular integral for different types of linear differential equations. It also discusses Legendre's linear equations, Cauchy-Euler equations, and solving simultaneous linear differential equations.
The document discusses the chain rule and Euler's theorem.
It explains the chain rule for functions of single, multiple, and general variables. The chain rule gives rules for finding the derivative of a composite function.
It also explains that if a function is homogeneous of degree k, its partial derivatives will be homogeneous of degree k-1. Euler's theorem relates the values of a homogeneous function to the values of its partial derivatives. The theorem is extended to functions of multiple variables.
Its states Periodic function, Fourier series for disontinous function, Fourier series, Intervals, Odd and even functions, Half range fourier series etc. Mostly used as active learning assignment in Degree 3rd sem students.
This document provides information on probability distributions and related concepts. It defines discrete and continuous random distributions. It explains probability distribution functions for discrete and continuous random variables and their properties. It also discusses mathematical expectation, variance, and examples of calculating these values for random variables.
Solving boundary value problems using the Galerkin's method. This is a weighted residual method, studied as an introduction to the Finite Element Method.
This is a part of a series on Advanced Numerical Methods.
Gauss Forward And Backward Central Difference Interpolation Formula Deep Dalsania
This PPT contains the topic called Gauss Forward And Backward Central Difference Interpolation Formula of subject called Numerical and Statistical Methods for Computer Engineering.
This document outlines the lecture schedule and topics for a course on Multivariate Calculus taught by Abdul Aziz. The course will cover partial derivatives of functions with two or more variables, including how to use partial derivatives to find maximum and minimum values. It will also discuss level curves, tangent planes, and rates of change for multivariate functions. Quizzes and exercises are included to help students practice these concepts.
1. The document discusses transformation of random variables, where a function g is applied to a random variable X to produce another random variable Y=g(X). It provides methods to find the density or distribution function of Y based on the density of X.
2. It examines two examples that use the distribution function method and density function method to find the density of Y when X has a standard normal distribution and Y is a transformation of X.
3. It introduces the Jacobian technique to generalize the density function method to problems with multiple inputs and outputs. The Jacobian allows transforming joint densities between different variable spaces using a determinant.
It is a new theory based on an algorithmic approach. Its only element
is called nokton. These rules are precise. The innities are completely
absent whatever the system studied. It is a theory with discrete space
and time. The theory is only at these beginnings.
The document discusses partial differentiation and its applications. It covers functions of two variables, first and second partial derivatives, and applications including the Cobb-Douglas production function and finding marginal productivity from a production function. Examples are provided to demonstrate calculating partial derivatives of various functions and applying partial derivatives in contexts like production analysis.
The document discusses derivatives and graphs. It defines interval notation used to indicate whether points are included or excluded from intervals. It then explains that the derivative of a function f(x) at a point x, f'(x), represents the slope of the tangent line to the graph of f(x) at (x, f(x)). Finally, it notes that points where the derivative is 0 are called critical points, as the tangent line is flat at these points.
The document discusses interpolation, which involves using a function to approximate values between known data points. It provides examples of Lagrange interpolation, which finds a polynomial passing through all data points, and Newton's interpolation, which uses divided differences to determine coefficients for approximating between points. The examples demonstrate constructing Lagrange and Newton interpolation polynomials using given data sets.
This document summarizes the Runge-Kutta methods for solving differential equations numerically. It introduces the first, second, third, and fourth order Runge-Kutta methods and provides the equations for calculating each. An example of using the fourth order Runge-Kutta method to solve the differential equation dy/dx=x+y is shown step-by-step. The example calculates the solution to y(0.2) given y(0)=1 using increments of h=0.1.
The document provides examples to illustrate how to find the eigenvalues and eigenvectors of a matrix.
1) For a 2x2 matrix, the characteristic polynomial is computed by taking the determinant of the matrix minus the identity matrix. The roots of the characteristic polynomial are the eigenvalues. The corresponding eigenvectors are found by solving the original eigenvalue equation.
2) For a triangular matrix, the eigenvalues are the diagonal elements. The eigenvectors are found by setting rows corresponding to non-diagonal elements to zero.
3) The document provides a numerical example to demonstrate finding the eigenvalues (3, 1, -2) and eigenvectors of a 3x3 matrix.
In mathematics, a partial derivative of a function of several variables is its derivative with respect to one of those variables, with the others held constant (as opposed to the total derivative, in which all variables are allowed to vary). Partial derivatives are used in vector calculus and differential geometry.
The document discusses integration by parts and the tableau method for evaluating integrals. It provides the formula for integration by parts, an example using the tableau method to evaluate the integral of (x3 + 2x)ex/2dx, and discusses how the tableau method can be used to evaluate integrals involving polynomials and functions that can be integrated repeatedly, such as ex, sin(x), and cos(x). It also provides the formulas for antiderivatives of trigonometric powers like cosn(x).
The document discusses several key concepts regarding derivatives:
(1) It explains how to use the derivative to determine if a function is increasing, decreasing, or neither on an interval using the signs of the derivative.
(2) It provides theorems and rules for finding local extrema (maxima and minima) of functions using the first and second derivative tests.
(3) It also discusses absolute extrema, monotonic functions, and the Rolle's Theorem and Mean Value Theorem which relate the derivative of a function to values of the function.
The document is notes for a lesson on partial derivatives. It introduces partial derivatives and their motivation as slopes of curves through a point on a multi-variable function. It defines partial derivatives mathematically and gives an example. It also discusses second partial derivatives and notes that mixed partials are always equal due to Clairaut's Theorem when the function is continuous. Finally, it provides an example of calculating second partial derivatives.
The document discusses vector spaces and subspaces. It defines vectors in Rn as n-tuples of real numbers and describes operations on vectors like addition and scalar multiplication. A vector space is a set with vectors that is closed under these operations and satisfies other axioms. Examples given include Rn, the space of matrices, and polynomial spaces. A subspace is a subset of a vector space that itself is a vector space under the operations in the larger space.
Linear differential equation with constant coefficientSanjay Singh
The document discusses linear differential equations with constant coefficients. It defines the order, auxiliary equation, complementary function, particular integral and general solution. It provides examples of determining the complementary function and particular integral for different types of linear differential equations. It also discusses Legendre's linear equations, Cauchy-Euler equations, and solving simultaneous linear differential equations.
The document discusses the chain rule and Euler's theorem.
It explains the chain rule for functions of single, multiple, and general variables. The chain rule gives rules for finding the derivative of a composite function.
It also explains that if a function is homogeneous of degree k, its partial derivatives will be homogeneous of degree k-1. Euler's theorem relates the values of a homogeneous function to the values of its partial derivatives. The theorem is extended to functions of multiple variables.
Its states Periodic function, Fourier series for disontinous function, Fourier series, Intervals, Odd and even functions, Half range fourier series etc. Mostly used as active learning assignment in Degree 3rd sem students.
This document provides information on probability distributions and related concepts. It defines discrete and continuous random distributions. It explains probability distribution functions for discrete and continuous random variables and their properties. It also discusses mathematical expectation, variance, and examples of calculating these values for random variables.
Solving boundary value problems using the Galerkin's method. This is a weighted residual method, studied as an introduction to the Finite Element Method.
This is a part of a series on Advanced Numerical Methods.
Gauss Forward And Backward Central Difference Interpolation Formula Deep Dalsania
This PPT contains the topic called Gauss Forward And Backward Central Difference Interpolation Formula of subject called Numerical and Statistical Methods for Computer Engineering.
This document outlines the lecture schedule and topics for a course on Multivariate Calculus taught by Abdul Aziz. The course will cover partial derivatives of functions with two or more variables, including how to use partial derivatives to find maximum and minimum values. It will also discuss level curves, tangent planes, and rates of change for multivariate functions. Quizzes and exercises are included to help students practice these concepts.
1. The document discusses transformation of random variables, where a function g is applied to a random variable X to produce another random variable Y=g(X). It provides methods to find the density or distribution function of Y based on the density of X.
2. It examines two examples that use the distribution function method and density function method to find the density of Y when X has a standard normal distribution and Y is a transformation of X.
3. It introduces the Jacobian technique to generalize the density function method to problems with multiple inputs and outputs. The Jacobian allows transforming joint densities between different variable spaces using a determinant.
It is a new theory based on an algorithmic approach. Its only element
is called nokton. These rules are precise. The innities are completely
absent whatever the system studied. It is a theory with discrete space
and time. The theory is only at these beginnings.
This document provides an introduction to the finite element method by first discussing the calculus of variations. It explains that the finite element formulation can be derived from a variational principle rather than an energy functional. It then presents three examples that illustrate functionals - the brachistochrone problem, geodesic problem, and isoperimetric problem. The document defines the concepts of extremal paths, varied paths, first variation, and the delta operator to derive the Euler-Lagrange equation, which provides the necessary condition for a functional to be extremized.
First principle, power rule, derivative of constant term, product rule, quotient rule, chain rule, derivatives of trigonometric functions and their inverses, derivatives of exponential functions and natural logarithmic functions, implicit differentiation, parametric differentiation, L'Hopital's rule
MA500-2: Topological Structures 2016
Aisling McCluskey, Daron Anderson
[email protected], [email protected]
Contents
0 Preliminaries 2
1 Topological Groups 8
2 Morphisms and Isomorphisms 15
3 The Second Isomorphism Theorem 27
4 Topological Vector Spaces 42
5 The Cayley-Hamilton Theorem 43
6 The Arzelà-Ascoli theorem 44
7 Tychonoff ’s Theorem if Time Permits 45
Continuous assessment 30%; final examination 70%. There will be a weekly
workshop led by Daron during which there will be an opportunity to boost
continuous assessment marks based upon workshop participation as outlined in
class.
This module is self-contained; the notes provided shall form the module text.
Due to the broad range of topics introduced, there is no recommended text.
However General Topology by R. Engelking is a graduate-level text which has
relevant sections within it. Also Undergraduate Topology: a working textbook by
McCluskey and McMaster is a useful revision text. As usual, in-class discussion
will supplement the formal notes.
1
0 PRELIMINARIES
0 Preliminaries
Reminder 0.1. A topology τ on the set X is a family of subsets of X, called
the τ-open sets, satisfying the three axioms.
(1) Both sets X and ∅ are τ-open
(2) The union of any subfamily is again a τ-open set
(3) The intersection of any two τ-open sets is again a τ-open set
We refer to (X,τ) as a topological space. Where there is no danger of ambi-
guity, we suppress reference to the symbol denoting the topology (in this case,
τ) and simply refer to X as a topological space and to the elements of τ as its
open sets. By a closed set we mean one whose complement is open.
Reminder 0.2. A metric on the set X is a function d: X×X → R satisfying
the five axioms.
(1) d(x,y) ≥ 0 for all x,y ∈ X
(2) d(x,y) = d(y,x) for x,y ∈ X
(3) d(x,x) = 0 for every x ∈ X
(4) d(x,y) = 0 implies x = y
(5) d(x,z) ≤ d(x,y) + d(y,z) for all x,y,z ∈ X
Axiom (5) is often called the triangle inequality.
Definition 0.3. If d′ : X × X → R satisfies axioms (1), (2), (3) and (5) but
maybe not (4) then we call it a pseudo-metric.
Reminder 0.4. Every metric on X induces a topology on X, called the metric
topology. We define an open ball to be a set of the form
B(x,r) = {y ∈ X : d(x,y) < r}
for any x ∈ X and r > 0. Then a subset G of X is defined to be open (wrt the
metric topology) if for each x ∈ G, there is r > 0 such that B(x,r) ⊂ G. Thus
open sets are arbitrary unions of open balls.
Topological Structures 2016 2 Version 0.15
0 PRELIMINARIES
The definition of the metric topology makes just as much sense when we are
working with a pseudo-metric. Open balls are defined in the same manner, and
the open sets are exactly the unions of open balls. Pseudo-metric topologies are
often neglected because they do not have the nice property of being Hausdorff.
Reminder 0.5. Suppose f : X → Y is a function between the topological
spaces X and Y . We say f is continuous to mean that whenever U is open in
Y ...
The document defines and discusses universal algebras. A universal algebra is a set with operations and relations defined on it. A subalgebra is a subset closed under the operations. The subalgebra generated by a set A, denoted [[A]], is the smallest subalgebra containing A. Morphisms between universal algebras are functions that preserve the operations and relations. Important concepts include products, quotients, and compatibility of operations. Examples of simple universal algebras with constants and 1-ary or 2-ary operations are given.
The document discusses methods for solving first order ordinary differential equations (ODEs). It covers:
1) Finding the integrating factor for exact differential equations.
2) Solving homogeneous first order linear ODEs by making a substitution to reduce it to a separable equation.
3) Solving inhomogeneous first order linear ODEs using an integrating factor.
Examples are provided to demonstrate each method step-by-step.
E[X|Y] represents the conditional expectation of the random variable X given Y. It is a random variable that is the "best approximation" of X given the information in Y. Some key properties of conditional expectation are:
1) E[X|Y] is a function of Y that provides the expected value of X for each value of Y.
2) E[X|Y] = E[X] if X and Y are independent.
3) Taking the expectation of E[X|Y] gives the unconditional expectation: E[E[X|Y]] = E[X].
On Certain Classess of Multivalent Functions iosrjce
In this we defined certain analytic p-valent function with negative type denoted by 휏푝
. We obtained
sharp results concerning coefficient bounds, distortion theorem belonging to the class 휏푝
.
The document discusses different types of difference operators and interpolation methods. It covers forward, backward, and central difference operators. It also covers Newton's interpolation method using forward and backward differences, Gauss's interpolation using forward and backward central differences, and Lagrange's interpolation for unequally spaced data. Stirling's formula for interpolation averages the results of Gauss' forward and backward formulas.
AEM Integrating factor to orthogonal trajactoriesSukhvinder Singh
This document provides information about integrating factors and their use in solving differential equations. It discusses:
1) How to find integrating factors by inspection, including common differential forms.
2) Four rules for finding integrating factors for exact and homogeneous differential equations.
3) Using integrating factors to solve linear differential equations and the Bernoulli equation.
4) The concept of orthogonal trajectories and the working rule for finding the differential equation of orthogonal trajectories given a family of curves.
An example of finding the orthogonal trajectories of the curve y = x2 + c is provided.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This document provides an introduction to dynamical systems and their mathematical modeling using differential equations. It discusses modeling dynamical systems using inputs, states, and outputs. It also covers simulating dynamical systems, equilibria, linearization, and system interconnections. Key topics include modeling dynamical systems using differential equations, the concept of inputs and outputs, interpreting mathematical models of dynamical systems, and converting higher-order models to first-order models.
This document discusses differentials and how they relate to differentiable functions. Some key points:
1. The differential of an independent variable x is defined as dx, which is equal to the increment Δx. The differential of a dependent variable y is defined as dy = f'(x) dx, where f'(x) is the derivative of the function.
2. Differentials allow approximations of changes in a function using derivatives, such as estimating errors or finding approximate roots.
3. Rules are provided for finding differentials of common functions using differentiation formulas. Examples demonstrate using differentials to estimate changes and approximate values.
The document introduces Euler's method for numerically solving ordinary differential equations. It provides the formulation of Euler's method as a recurrence relation and gives examples of applying the method to solve various initial value problems by discretizing the interval and time steps. Euler's method approximates the slope of the tangent line at each step to iteratively calculate subsequent y-values.
The document introduces Euler's method for numerically solving ordinary differential equations. It provides the formulation of Euler's method as a recurrence relation and gives examples of applying the method to solve various initial value problems by discretizing the interval and time steps. Euler's method approximates the slope of the tangent line at each step to iteratively calculate subsequent y-values.
The document discusses the chain rule and how to apply it to find derivatives of composite functions. It provides examples of using the chain rule to differentiate exponential functions, power functions, and functions that require multiple applications of the chain rule. Formulas for differentiation using the chain rule are generalized to functions of x raised to a variable power or inside an exponential, logarithm, or other functions.
This document discusses differential Taylor transformations and their applications. It defines forward and reverse differential Taylor transformations, which allow functions to be represented as discrete power series. The basic properties of these transformations are described, including how images relate to sums, products, derivatives, integrals and ratios of the original functions. Examples are provided to demonstrate how complicated non-linear functions can be transformed. Determinant calculations of non-autonomous matrices can also be performed based on these differential-Taylor transformations.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Traditional Musical Instruments of Arunachal Pradesh and Uttar Pradesh - RAYH...
Euler theorems
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49
THEOREMS
Homogeneous Function in two variables: A function u is said to be a homogeneous function in x and y
of degree n if it is expressible as
u = xn
𝛟 (y/x) where 𝛟 (y/x) is a function of y/x
1) Euler’s Theorem for u :
If u is a homogeneous function in x and y of degree ‘n’, then show that x
𝝏𝒖
𝝏𝒙
+ y
𝝏𝒖
𝝏𝒚
= nu.
Proof: Since u is a homogeneous function in x and y of degree n,
Let u = xn
𝛟 (y/x) ……….(I)
Differentiating (I) partially with respect to x,
𝝏𝒖
𝝏𝒙
= nxn- 1
𝛟 (y/x) + xn
𝛟’ (y/x) (- y/x2
)
= nxn- 1
𝛟 (y/x) – y xn-2
𝛟’ (y/x)……….. (i)
Also, differentiating (I) partially with respect to y,
𝝏𝒖
𝝏𝒚
= xn
𝛟’ (y/x) 1/x
= xn- 1
𝛟’ (y/x) …………(ii)
Multiplying eq. (i) by eq. (ii) by y and adding we get,
x
𝝏𝒖
𝝏𝒙
+ y
𝝏𝒖
𝝏𝒚
= nxn
𝛟 (y/x) – yxn-1
𝛟’ (y/x) + yxn- 1
𝛟’ (y/x)
= n xn
𝛟 (y/x)
= nu [ from (I) ]
Hence x
𝜕𝑢
𝜕𝑥
+ y
𝜕𝑢
𝜕𝑦
= nu
2) Deduction:
If u is an homogeneous function in x and y of degree n,
then show that x2 𝝏 𝟐 𝒖
𝝏𝒙 𝟐 + 2xy
𝝏 𝟐 𝒖
𝝏𝒙𝝏𝒚
+ y2 𝝏 𝟐 𝒖
𝝏𝒚 𝟐 = n (n-1) u
Proof : Since, u is an homogeneous function in x and y of degree n,
by Euler’s Theorem we have,
x
𝜕𝑢
𝜕𝑥
+ y
𝜕𝑢
𝜕𝑦
= nu …………… (I)
Differentiating (I) partially with respect to x we get,
x
𝜕2 𝑢
𝜕𝑥2 +
𝜕𝑢
𝜕𝑥
+ y
𝜕2 𝑢
𝜕𝑥𝜕𝑦
= n
𝜕𝑢
𝜕𝑥
x
𝜕2 𝑢
𝜕𝑥2 + y
𝜕2 𝑢
𝜕𝑥𝜕𝑦
= (n- 1)
𝜕𝑢
𝜕𝑥
…………….. (II)
Similarly differentiating (I) partially w. r. t. y we get,
y
𝜕2 𝑢
𝜕𝑦2 + x
𝜕2 𝑢
𝜕𝑦𝜕𝑥
= (n- 1)
𝜕𝑢
𝜕𝑦
………… (III)
Multiplying eq. (II) by x and eq. (III) by y and adding we get,
x2 𝜕2 𝑢
𝜕𝑥2 + 2xy
𝜕2 𝑢
𝜕𝑥𝜕𝑦
+ y2 𝜕2 𝑢
𝜕𝑦2 = (n- 1) x
𝜕𝑢
𝜕𝑥
+ (n- 1) y
= (n- 1) (x
𝜕𝑢
𝜕𝑥
+ y
𝜕𝑢
𝜕𝑦
)
= (n- 1) nu [from (I) ]
x2 𝜕2 𝑢
𝜕𝑥2 + 2xy
𝜕2 𝑢
𝜕𝑥𝜕𝑦
+ y2 𝜕2 𝑢
𝜕𝑦2 = n (n- 1) u
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3) Euler’s Theorem for f(u):
If f (u) is an homogeneous function in x and y of degree n then show that x
𝝏𝒖
𝝏𝒙
+ y
𝝏𝒖
𝝏𝒚
= n
𝒇(𝒖)
𝒇′(𝒖)
Proof : Let z = f(u)
Now f(u) is an homogeneous function in x and y of degree n.
z is an homogeneous function in x and y of degree n.
By Euler’s Theorem we have,
x
𝝏𝒛
𝝏𝒙
+ y
𝝏𝒛
𝝏𝒚
= nz……………………(I)
But
𝝏𝒛
𝝏𝒙
= f ’(u)
𝝏𝒖
𝝏𝒙
and
𝝏𝒛
𝝏𝒚
= f ’(u)
𝝏𝒖
𝝏𝒚
Substituting in (I) we get,
xf ’(u)
𝝏𝒖
𝝏𝒙
+ yf ’(u)
𝝏𝒖
𝝏𝒚
= nf(u) [Since z = f(u) ]
x
𝝏𝒖
𝝏𝒙
+ y
𝝏𝒖
𝝏𝒚
= n
𝒇𝒖
𝒇′(𝒖)
4) Deduction:
If f (u) is an homogeneous function in x and y of degree n then,
show that, x2 𝝏 𝟐 𝒖
𝝏𝒙 𝟐 + 2xy
𝝏 𝟐 𝒖
𝝏𝒙𝝏𝒚
+ y2 𝝏 𝟐 𝒖
𝝏𝒚 𝟐 = G(u) [G ’(u) – 1] where G ’(u) = n
𝒇𝒖
𝒇′(𝒖)
Proof : Since f(u) is an homogeneous function in x and y of degree n,
by Euler’s Theorem we have
x
𝜕𝑢
𝜕𝑥
+ y
𝜕𝑢
𝜕𝑦
= n
𝑓(𝑢)
𝑓′(𝑢)
= G(u) (say) ………..(I)
Differentiating (I) partially with respect to x,
x
𝜕2 𝑢
𝜕𝑥2 +
𝜕𝑢
𝜕𝑦
+ y
𝜕2 𝑢
𝜕𝑥𝜕𝑦
= G ’(u).
𝜕𝑢
𝜕𝑥
x
𝜕2 𝑢
𝜕𝑥2 + y
𝜕2 𝑢
𝜕𝑥 𝜕𝑦 2 = [ G ’(u) – 1 ]
𝜕𝑢
𝜕𝑥
…………(II)
Similarly differentiating (I) partially with respect to y,
x
𝜕2 𝑢
𝜕𝑦𝜕𝑥
+ y
𝜕2 𝑢
𝜕𝑦2 = [ G ’ (u) – 1 ]
𝜕𝑢
𝜕𝑦
…………(III)
Multiplying eq. (II) by x and eq. (III) by y and adding we get,
x2 𝜕2 𝑢
𝜕𝑥2 + 2xy
𝜕2 𝑢
𝜕𝑥𝜕𝑦
+ y2 𝜕2 𝑢
𝜕𝑦2 = [ G ’(u) – 1 ] x
𝜕𝑢
𝜕𝑥
+ [ G ’(u) – 1 ] y
𝜕𝑢
𝜕𝑦
= [ G ’(u) – 1 ] (x
𝜕𝑢
𝜕𝑥
+ y
𝜕𝑢
𝜕𝑦
)
= [ G ’(u) – 1 ] G(u) …….[ From (I)]
Hence x2 𝜕2 𝑢
𝜕𝑥2 + 2xy
𝜕2 𝑢
𝜕𝑥𝜕𝑦
+ y2 𝜕2 𝑢
𝜕𝑦2 = G(u) [ G ’(u) – 1 ] where G ’(u) = n
𝑓𝑢
𝑓′(𝑢)
Homogeneous function in three variables: A function u is said to be an homogeneous function in x, y, z
degree n if it expressible as u = xn
𝛟 (y/x, z/x)
5) Euler’s Theorem in three variables
If u is an homogeneous function in x, y, z of degree n, then show that x
𝝏𝒖
𝝏𝒙
+ 𝒚
𝝏𝒖
𝝏𝒚
+ z
𝝏𝒖
𝝏𝒛
= nu
Proof: Since u is an homogeneous function in x, y, z of degree n,
Let u = xn
𝛟 (y/x, z/x ) ……..(I)
If p = y/x and q = z/x
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Then u = xn
𝛟 (p, q) where p and q are functions of x, y, z
Now
𝝏𝒑
𝝏𝒙
= -
𝒚
𝒙 𝟐 ,
𝝏𝒑
𝝏𝒚
= 1/x,
𝝏𝒑
𝝏𝒛
= 0
𝜕𝑞
𝜕𝑥
= -
𝑧
𝑥2 ,
𝜕𝑞
𝜕𝑦
= 0,
𝜕𝑞
𝜕𝑧
= 1/x
Then
𝝏𝒖
𝝏𝒙
=
𝝏
𝝏𝒙
[ xn
𝛟 (p, q) ]
=
𝝏
𝝏𝒙
[ xn
]. 𝛟 (p, q) + xn
.
𝝏
𝝏𝒙
[ (p, q) ]
= nxn-1
𝛟 (p, q) + xn
(
𝜕𝜙
𝜕𝑝
.
𝜕𝑝
𝜕𝑥
+
𝜕𝜙
𝜕𝑞
.
𝜕𝑞
𝜕𝑥
)
= n xn-1
𝛟 (p, q) + xn
(−
𝑦
𝑥2 .
𝜕𝜙
𝜕𝑝
-
𝑧
𝑥2 .
𝜕𝜙
𝜕𝑞
)
= n xn-1
𝛟 (p, q) – y xn- 2 𝜕𝜙
𝜕𝑝
– zxn- 2 𝜕𝜙
𝜕𝑞
…………….(i)
Also
𝜕𝑢
𝜕𝑦
= xn 𝜕
𝜕𝑦
[𝛟 (p, q) ]
= xn
(
𝜕𝜙
𝜕𝑝
.
𝜕𝑝
𝜕𝑦
+
𝜕𝜙
𝜕𝑞
.
𝜕𝑞
𝜕𝑦
)
= xn
(1/x
𝜕𝜙
𝜕𝑝
+0
𝜕𝜙
𝜕𝑞
)
= xn-1 𝜕𝜙
𝜕𝑝
………………(ii)
Similarly
𝜕𝑢
𝜕𝑧
= xn-1 𝜕𝜙
𝜕𝑞
………………(iii)
Multiplying eq. (i) by x, eq. (ii) by y, eq. (iii) by z and adding we get,
x
𝜕𝑢
𝜕𝑥
+ 𝑦
𝜕𝑢
𝜕𝑦
+ z
𝜕𝑢
𝜕𝑧
= nxn
𝛟 (p, q) – yxn-1 𝜕𝜙
𝜕𝑝
- zxn-1 𝜕𝜙
𝜕𝑞
+ yxn-1 𝜕𝜙
𝜕𝑝
+zxn-1 𝜕𝜙
𝜕𝑞
= n xn
(p, q)
= n u [From (I)]
Hence x
𝜕𝑢
𝜕𝑥
+ 𝑦
𝜕𝑢
𝜕𝑦
+ z
𝜕𝑢
𝜕𝑧
= n u