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Lecture

Derivatives, O-Notation

In course

MATH-101(de): Analysis I (German)

Es werden die Grundlagen der Analysis sowie der Differential- und Integralrechnung von Funktionen einer reellen Veränderlichen erarbeitet. ...

Description

This lecture covers the concepts of left and right derivatives, O-Notation, and extrema in Analysis 1. It explains the rules for derivatives, the definition of left and right derivatives, and their applications. The lecture also delves into the complexity of sorting algorithms, such as Bubble Sort and Merge Sort, and the complexity of matrix multiplication. Additionally, it discusses the use of the O-Notation in analyzing the complexity of algorithms.

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Instructors (3)

Tobias Johannes Schmid

Joachim Krieger

Daniel Kressner

Official source

Ontological neighbourhood

Theoretical computer science

Algorithms and data structures: Analysis of algorithms

Related concepts (32)

Big O notation

Big O notation is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. Big O is a member of a family of notations invented by Paul Bachmann, Edmund Landau, and others, collectively called Bachmann–Landau notation or asymptotic notation. The letter O was chosen by Bachmann to stand for Ordnung, meaning the order of approximation. In computer science, big O notation is used to classify algorithms according to how their run time or space requirements grow as the input size grows.

L-notation

L-notation is an asymptotic notation analogous to big-O notation, denoted as for a bound variable tending to infinity. Like big-O notation, it is usually used to roughly convey the rate of growth of a function, such as the computational complexity of a particular algorithm. It is defined as where c is a positive constant, and is a constant . L-notation is used mostly in computational number theory, to express the complexity of algorithms for difficult number theory problems, e.g.

Maximum and minimum

In mathematical analysis, the maximum and minimum of a function are, respectively, the largest and smallest value taken by the function. Known generically as extremum, they may be defined either within a given range (the local or relative extrema) or on the entire domain (the global or absolute extrema) of a function. Pierre de Fermat was one of the first mathematicians to propose a general technique, adequality, for finding the maxima and minima of functions.

Calculus of variations

The calculus of variations (or variational calculus) is a field of mathematical analysis that uses variations, which are small changes in functions and functionals, to find maxima and minima of functionals: mappings from a set of functions to the real numbers. Functionals are often expressed as definite integrals involving functions and their derivatives. Functions that maximize or minimize functionals may be found using the Euler–Lagrange equation of the calculus of variations.

Sorting algorithm

In computer science, a sorting algorithm is an algorithm that puts elements of a list into an order. The most frequently used orders are numerical order and lexicographical order, and either ascending or descending. Efficient sorting is important for optimizing the efficiency of other algorithms (such as search and merge algorithms) that require input data to be in sorted lists. Sorting is also often useful for canonicalizing data and for producing human-readable output.

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Matrix Multiplication and Divide-and-Conquer Techniques

Discusses matrix multiplication using divide-and-conquer techniques and introduces Strassen's algorithm for improved efficiency.

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