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Principles of mathematical analysis

Walter Rudin
TLDR
The real and complex number system as discussed by the authors is a real number system where the real number is defined by a real function and the complex number is represented by a complex field of functions.
Abstract
Chapter 1: The Real and Complex Number Systems Introduction Ordered Sets Fields The Real Field The Extended Real Number System The Complex Field Euclidean Spaces Appendix Exercises Chapter 2: Basic Topology Finite, Countable, and Uncountable Sets Metric Spaces Compact Sets Perfect Sets Connected Sets Exercises Chapter 3: Numerical Sequences and Series Convergent Sequences Subsequences Cauchy Sequences Upper and Lower Limits Some Special Sequences Series Series of Nonnegative Terms The Number e The Root and Ratio Tests Power Series Summation by Parts Absolute Convergence Addition and Multiplication of Series Rearrangements Exercises Chapter 4: Continuity Limits of Functions Continuous Functions Continuity and Compactness Continuity and Connectedness Discontinuities Monotonic Functions Infinite Limits and Limits at Infinity Exercises Chapter 5: Differentiation The Derivative of a Real Function Mean Value Theorems The Continuity of Derivatives L'Hospital's Rule Derivatives of Higher-Order Taylor's Theorem Differentiation of Vector-valued Functions Exercises Chapter 6: The Riemann-Stieltjes Integral Definition and Existence of the Integral Properties of the Integral Integration and Differentiation Integration of Vector-valued Functions Rectifiable Curves Exercises Chapter 7: Sequences and Series of Functions Discussion of Main Problem Uniform Convergence Uniform Convergence and Continuity Uniform Convergence and Integration Uniform Convergence and Differentiation Equicontinuous Families of Functions The Stone-Weierstrass Theorem Exercises Chapter 8: Some Special Functions Power Series The Exponential and Logarithmic Functions The Trigonometric Functions The Algebraic Completeness of the Complex Field Fourier Series The Gamma Function Exercises Chapter 9: Functions of Several Variables Linear Transformations Differentiation The Contraction Principle The Inverse Function Theorem The Implicit Function Theorem The Rank Theorem Determinants Derivatives of Higher Order Differentiation of Integrals Exercises Chapter 10: Integration of Differential Forms Integration Primitive Mappings Partitions of Unity Change of Variables Differential Forms Simplexes and Chains Stokes' Theorem Closed Forms and Exact Forms Vector Analysis Exercises Chapter 11: The Lebesgue Theory Set Functions Construction of the Lebesgue Measure Measure Spaces Measurable Functions Simple Functions Integration Comparison with the Riemann Integral Integration of Complex Functions Functions of Class L2 Exercises Bibliography List of Special Symbols Index

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Journal ArticleDOI

The Effect of Corruption on Bidding Behavior in First-Price Auctions

TL;DR: In this paper, the consequences of corruption on efficiency, bidders' welfare and expected revenue were analyzed in a first-price auction, where one bidder is granted a right of first refusal.
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Logarithmic Correction for the Susceptibility of the 4-Dimensional Weakly Self-Avoiding Walk: A Renormalisation Group Analysis

TL;DR: In this paper, it was shown that the continuous-time weakly self-avoiding walk has a logarithmic correction to mean-field scaling behavior as the critical point is approached, with exponent of 1/4 for the logrithm.
Journal ArticleDOI

Robot ecology: Constraint-based control design for long duration autonomy

TL;DR: Drawing inspiration from ecology, which concerns itself with the strong coupling between organism and the environment it inhabits, “survival constraints” are encoded through Boolean compositions of multiple control barrier functions, and it is shown how this construction supports long duration autonomy in the context of persistent environmental monitoring on a team of mobile robots.
Journal ArticleDOI

Description of the minimizers of least squares regularized~ with~ $\bm{\ell_0}$-norm. Uniqueness of the global minimizer

TL;DR: The theoretical contributions, summarized below, shed new light on the existing algorithms and can help in the conception of innovative numerical schemes.
Proceedings ArticleDOI

Additive non-Gaussian noise channels: mutual information and conditional mean estimation

TL;DR: It is found that, for every fixed input distribution, the derivative of the mutual information with respect to the signal strength is equal to the correlation of two conditional mean estimates associated with the input and the noise respectively.