Concept

Big O notation

Related lectures (27)

Derivatives Rules: Notation, Extrema

Covers the rules of derivatives, O-notation, and extrema in the context of theorem 6.5 and examples.

Derivatives, O-Notation

Explores derivatives, O-Notation, extrema, and algorithm complexity in Analysis 1.

Complexity of Algorithms: Big-Omega and Big-Theta

Explains Big-Omega, Big-Theta, and Little-o notations for functions and polynomials.

Algorithmic Complexity: Visualization and Analysis

Explores algorithmic complexity, visualization of functions, and algorithm efficiency analysis using Python.

Mean Value Theorem (Mittelwertsatz)

Covers the Mean Value Theorem in differential calculus, focusing on critical points and global extrema within intervals.

Computation & Algorithms I

Explores algorithm complexity, time complexity estimation, Landau notations, and asymptotic behavior analysis.

O-Notation, Local Extrema

Covers O-Notation, local extrema, and critical points in functions.

Time Complexity and Landau Notation: Basics

Covers the basics of time complexity and Landau notation, focusing on understanding the concepts and their applications in algorithms.

Introduction to Logarithm

Covers the introduction to logarithms, algorithmic notations, and analysis of algorithm travel times.

Complexity of Algorithms: Big-Omega and Big-Theta

Explains Big-Omega, Big-Theta, and Little-o notations for functions and polynomials.

Complexity of Algorithms

Explores the complexity of algorithms, including big-O notation and efficiency analysis.

Algorithms: Problem Solving and Graph Algorithms

Covers elementary graph algorithms, a midterm exam on algorithmic problem-solving, and distance measurement between strings.

Algorithmic Complexity: Theta Notation

Explores algorithmic complexity, comparing growth rates using Theta notation and characterizing different complexity classes.

Discussion of Complexity

Explores worst-case complexity in computer science and the importance of real-life complexity in algorithm selection.

Algorithm Analysis: Time Complexity

Covers algorithm temporal complexity evaluation and Big Theta notation for large input data.

Trivial Group: Properties and Notation

Covers properties and notation of the trivial group and discusses the uniqueness of elements.

Algorithmic Complexity: Travel Time Analysis

Covers algorithmic complexity and travel time analysis, focusing on measuring the time taken by algorithms and evaluating their performance.

Maximum Subarray Problem

Covers the Master method, maximum-subarray problem, and divide-and-conquer algorithmic paradigm.

Complexity of Algorithms

Covers the Big-O notation to analyze algorithm efficiency and provides examples of polynomial and factorial function estimates.

Complexity of Algorithms: Growth of Functions

Analyzes the growth of functions to understand algorithm complexity and efficiency.

Related lectures (27)

Derivatives Rules: Notation, Extrema

Covers the rules of derivatives, O-notation, and extrema in the context of theorem 6.5 and examples.

Derivatives, O-Notation

Explores derivatives, O-Notation, extrema, and algorithm complexity in Analysis 1.

Complexity of Algorithms: Big-Omega and Big-Theta

Explains Big-Omega, Big-Theta, and Little-o notations for functions and polynomials.

Algorithmic Complexity: Visualization and Analysis

Explores algorithmic complexity, visualization of functions, and algorithm efficiency analysis using Python.

Mean Value Theorem (Mittelwertsatz)

Covers the Mean Value Theorem in differential calculus, focusing on critical points and global extrema within intervals.

Computation & Algorithms I

Explores algorithm complexity, time complexity estimation, Landau notations, and asymptotic behavior analysis.

O-Notation, Local Extrema

Covers O-Notation, local extrema, and critical points in functions.

Time Complexity and Landau Notation: Basics

Covers the basics of time complexity and Landau notation, focusing on understanding the concepts and their applications in algorithms.

Introduction to Logarithm

Covers the introduction to logarithms, algorithmic notations, and analysis of algorithm travel times.

Complexity of Algorithms: Big-Omega and Big-Theta

Explains Big-Omega, Big-Theta, and Little-o notations for functions and polynomials.

Complexity of Algorithms

Explores the complexity of algorithms, including big-O notation and efficiency analysis.

Algorithms: Problem Solving and Graph Algorithms

Covers elementary graph algorithms, a midterm exam on algorithmic problem-solving, and distance measurement between strings.

Algorithmic Complexity: Theta Notation

Explores algorithmic complexity, comparing growth rates using Theta notation and characterizing different complexity classes.

Discussion of Complexity

Explores worst-case complexity in computer science and the importance of real-life complexity in algorithm selection.

Algorithm Analysis: Time Complexity

Covers algorithm temporal complexity evaluation and Big Theta notation for large input data.

Trivial Group: Properties and Notation

Covers properties and notation of the trivial group and discusses the uniqueness of elements.

Algorithmic Complexity: Travel Time Analysis

Covers algorithmic complexity and travel time analysis, focusing on measuring the time taken by algorithms and evaluating their performance.

Maximum Subarray Problem

Covers the Master method, maximum-subarray problem, and divide-and-conquer algorithmic paradigm.

Complexity of Algorithms

Covers the Big-O notation to analyze algorithm efficiency and provides examples of polynomial and factorial function estimates.

Complexity of Algorithms: Growth of Functions

Analyzes the growth of functions to understand algorithm complexity and efficiency.