Uniform convergence is a powerful tool for integrating infinite series. It allows us to swap the order of integration and summation, making complex calculations easier. This concept builds on earlier ideas about convergence, extending them to functions and integrals.

Understanding when and how to apply uniform convergence to integration is crucial. It helps us solve problems involving infinite series and improves our grasp of advanced calculus concepts. Mastering this topic opens doors to more advanced mathematical analysis techniques.

Integration of Uniformly Convergent Series

Theorem Statement and Proof

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  • The theorem states that if a series of functions n=1fn(x)\sum_{n=1}^{\infty} f_n(x) converges uniformly to f(x)f(x) on [a,b][a,b], then the series can be integrated term by term on [a,b][a,b]
  • Mathematically, if n=1fn(x)\sum_{n=1}^{\infty} f_n(x) converges uniformly to f(x)f(x) on [a,b][a,b], then abf(x)dx=n=1abfn(x)dx\int_a^b f(x) dx = \sum_{n=1}^{\infty} \int_a^b f_n(x) dx
  • To prove the theorem, consider the partial sums Sn(x)=k=1nfk(x)S_n(x) = \sum_{k=1}^n f_k(x) and their integrals abSn(x)dx=k=1nabfk(x)dx\int_a^b S_n(x) dx = \sum_{k=1}^n \int_a^b f_k(x) dx
    • Since n=1fn(x)\sum_{n=1}^{\infty} f_n(x) converges uniformly to f(x)f(x), for any ε>0\varepsilon > 0, there exists an NN such that Sn(x)f(x)<ε|S_n(x) - f(x)| < \varepsilon for all nNn \geq N and all x[a,b]x \in [a,b]
    • Integrating the inequality yields abSn(x)dxabf(x)dxabSn(x)f(x)dx<ε(ba)|\int_a^b S_n(x) dx - \int_a^b f(x) dx| \leq \int_a^b |S_n(x) - f(x)| dx < \varepsilon(b-a) for all nNn \geq N
    • This proves that abSn(x)dx\int_a^b S_n(x) dx converges to abf(x)dx\int_a^b f(x) dx as nn \rightarrow \infty, establishing the theorem

Conditions for Term-by-Term Integration

  • A uniformly convergent series n=1fn(x)\sum_{n=1}^{\infty} f_n(x) can be integrated term by term on [a,b][a,b] if the following conditions are met:
    • Each function fn(x)f_n(x) is integrable on [a,b][a,b]
    • The series n=1fn(x)\sum_{n=1}^{\infty} f_n(x) converges uniformly on [a,b][a,b]
  • If these conditions are satisfied, then abf(x)dx=n=1abfn(x)dx\int_a^b f(x) dx = \sum_{n=1}^{\infty} \int_a^b f_n(x) dx, where f(x)f(x) is the limit function of the series
  • The uniform convergence of the series is crucial for the interchange of the integral and the sum to be valid
  • Examples of series that can be integrated term by term include:
    • Power series within their interval of convergence
    • Fourier series of continuous functions on a closed interval

Term-by-Term Integration of Series

Evaluating Integrals with Uniform Convergence

  • To evaluate an integral involving a uniformly convergent series, first check if the series converges uniformly on the given interval
  • If the series converges uniformly, integrate the series term by term using the theorem on the integration of uniformly convergent series
  • Calculate the integrals of the individual terms abfn(x)dx\int_a^b f_n(x) dx and find the sum of the resulting series n=1abfn(x)dx\sum_{n=1}^{\infty} \int_a^b f_n(x) dx
  • The sum of the integrated terms will equal the integral of the limit function abf(x)dx\int_a^b f(x) dx
  • Example: Evaluate 01n=1xnn2dx\int_0^1 \sum_{n=1}^{\infty} \frac{x^n}{n^2} dx
    • The series n=1xnn2\sum_{n=1}^{\infty} \frac{x^n}{n^2} converges uniformly on [0,1][0,1] by the
    • Integrate term by term: 01n=1xnn2dx=n=101xnn2dx=n=11n2(n+1)=π261\int_0^1 \sum_{n=1}^{\infty} \frac{x^n}{n^2} dx = \sum_{n=1}^{\infty} \int_0^1 \frac{x^n}{n^2} dx = \sum_{n=1}^{\infty} \frac{1}{n^2(n+1)} = \frac{\pi^2}{6} - 1

Uniform Convergence and Interchanging Limits

  • Uniform convergence is a sufficient condition for the interchange of limits and integrals
  • If a sequence of functions {fn(x)}\{f_n(x)\} converges uniformly to f(x)f(x) on [a,b][a,b], then limnabfn(x)dx=ablimnfn(x)dx=abf(x)dx\lim_{n \rightarrow \infty} \int_a^b f_n(x) dx = \int_a^b \lim_{n \rightarrow \infty} f_n(x) dx = \int_a^b f(x) dx
  • This property allows for the evaluation of integrals involving limits by first interchanging the limit and the integral and then evaluating the limit
  • Without uniform convergence, the interchange of limits and integrals may not be valid, and counterexamples exist where the equality fails to hold
  • Example: Consider the sequence of functions fn(x)=nx1+n2x2f_n(x) = \frac{nx}{1+n^2x^2} on [0,1][0,1]
    • limnfn(x)=0\lim_{n \rightarrow \infty} f_n(x) = 0 for all x[0,1]x \in [0,1], but the convergence is not uniform
    • limn01fn(x)dx=π4\lim_{n \rightarrow \infty} \int_0^1 f_n(x) dx = \frac{\pi}{4}, while 01limnfn(x)dx=0\int_0^1 \lim_{n \rightarrow \infty} f_n(x) dx = 0, showing that the interchange of limit and integral is not valid in this case

Key Terms to Review (16)

: The symbol ∫ represents the integral in mathematics, which is a fundamental concept for calculating the area under curves or the accumulation of quantities. Integrals can be defined in various ways, with Riemann integrals focusing on partitioning intervals and summing up areas of rectangles, while also playing a crucial role in connecting derivatives and integration through the Fundamental Theorem of Calculus.
Absolute convergence: Absolute convergence refers to a series that converges when the absolute values of its terms are summed. This concept is crucial because if a series converges absolutely, it guarantees that the series converges regardless of the arrangement of its terms, linking it to various properties and tests of convergence for series and functions.
Absolutely convergent series: An absolutely convergent series is a series whose absolute terms converge, meaning that the series formed by taking the absolute value of each term converges. This type of convergence implies that the original series also converges, and importantly, it allows for the rearrangement of terms without affecting the sum. Understanding this concept is crucial when dealing with uniformly convergent series and their integration properties.
Bernhard Riemann: Bernhard Riemann was a German mathematician whose work laid the foundations for several important areas in mathematics, particularly in analysis and geometry. He is best known for his contributions to the concept of integration, which is crucial for understanding how to calculate areas under curves and the behavior of functions. His ideas extend to the convergence of sequences and series, providing essential tools for studying continuity and differentiability.
Cauchy Criterion: The Cauchy Criterion states that a sequence is convergent if and only if it is a Cauchy sequence, meaning that for every positive number $$ heta$$, there exists a natural number $$N$$ such that for all natural numbers $$m, n > N$$, the absolute difference between the terms is less than $$ heta$$. This concept helps in analyzing convergence without necessarily knowing the limit, linking it to various properties of functions, sequences, and series.
Conditionally convergent series: A conditionally convergent series is a series that converges when its terms are added in a specific order, but diverges if the terms are rearranged. This means that while the sum of the series exists, the order of the summands affects the result, highlighting an essential property of certain infinite series. This concept is crucial in understanding how convergence behaves under different conditions, especially when dealing with uniformly convergent series and their integration.
Convergence Criteria: Convergence criteria refer to the specific conditions or tests used to determine whether a series or sequence converges to a limit. In mathematical analysis, these criteria help establish the behavior of functions represented by series, especially when discussing the interchange of limits and integration for uniformly convergent series.
Dominated Convergence Theorem: The Dominated Convergence Theorem is a fundamental result in measure theory and integration that allows one to interchange limits and integrals under certain conditions. Specifically, it states that if a sequence of measurable functions converges almost everywhere to a function and is dominated by an integrable function, then the integral of the limit is equal to the limit of the integrals. This theorem is especially important when dealing with uniformly convergent series as it helps in evaluating the convergence of integrals that may not be straightforward.
Epsilon-delta argument: An epsilon-delta argument is a formal mathematical framework used to define the limit of a function rigorously. It involves two parameters, epsilon (\(\epsilon\)), which represents a desired level of accuracy, and delta (\(\delta\)), which defines how close the input values must be to a specific point in order to ensure that the function's output is within that desired accuracy. This method is foundational in establishing concepts such as continuity, differentiability, and integration, particularly when discussing uniformly convergent series.
Henri Léon Lebesgue: Henri Léon Lebesgue was a French mathematician renowned for his contributions to measure theory and integration, specifically the development of the Lebesgue integral. His work laid the foundation for understanding integration in a more general sense, allowing for the integration of a broader class of functions compared to the traditional Riemann integral, which is particularly relevant when considering uniformly convergent series.
Lebesgue Integrability: Lebesgue integrability refers to a function being integrable in the sense of the Lebesgue integral, which means it can be measured and summed over its domain using Lebesgue measure. This concept is broader than traditional Riemann integrability, allowing for the integration of more complex functions and accommodating functions with discontinuities or defined only on sets of measure zero. Understanding Lebesgue integrability is crucial for advanced analysis and connects closely to the criteria for determining when a function can be integrated and how uniformly convergent series can be treated.
Linearity of Integration: Linearity of integration refers to the property that allows the integral of a sum of functions to be expressed as the sum of their integrals, and the integral of a constant multiplied by a function to be equal to the constant multiplied by the integral of that function. This principle simplifies the process of integration, making it easier to work with complex functions by breaking them down into simpler components.
Monotone Convergence: Monotone convergence refers to a property of sequences where the terms are either non-decreasing or non-increasing, leading to the conclusion that such sequences will converge to a limit. This concept is important in analysis as it assures us that if a sequence consistently increases or decreases, it will stabilize at some value, making it easier to analyze the behavior of series and integrals.
Pointwise Convergence: Pointwise convergence refers to a type of convergence of a sequence of functions where, for each point in the domain, the sequence converges to the value of a limiting function. This means that for every point, as you progress through the sequence, the values get closer and closer to the value defined by the limiting function. Pointwise convergence is crucial in understanding how functions behave under limits and is often contrasted with uniform convergence, which has different implications for continuity and integration.
Weierstrass M-test: The Weierstrass M-test is a method used to determine the uniform convergence of a series of functions. It states that if a series of functions converges pointwise and is bounded above by a convergent series of non-negative constants, then the original series converges uniformly. This test connects the ideas of pointwise convergence and uniform convergence and plays a critical role in analysis, especially when dealing with series of functions.
σ: In mathematical analysis, the symbol σ often represents a measure or a summation index in various contexts, indicating a systematic approach to aggregating values. This term is crucial in understanding Riemann sums, where σ can denote partition indices, and it also plays a significant role in series convergence, such as when discussing Taylor series and their coefficients. Additionally, σ is linked to the conditions under which certain integrals converge uniformly, highlighting its versatility across different areas of mathematical analysis.
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