Asymptotic notation Study guides, Class notes & Summaries

1 Problem 1 Solve the following recurrence relation using any method. Provide your answer in big-O notation: T(n) = 2T( n 2 ) + log(n) for n > 1, 0 otherwise • T(n) = O(n) • T(n) = O(nlogn) • T(n) = O(n 2 ) • T(n) = O(logn) 1.1 Rationale This recurrence relation can be tricky to solve using iterative substitution or tree-based methods. It’s best to use the Master theorem here. Recall the form: T(n) = aT(n/b) + f(n) Since f(n) = log(n) = n  where  < log2(2) = 1...

DSA

Goes over asymptotic notation. Big-O and how to prove it, and some different algorithms.

My document of DSA notes is a comprehensive guide to data structures and algorithms for computer science and programming. It covers a wide range of topics such as complexity analysis, algorithm design techniques, and advanced data structures. The notes include detailed explanations of various data structures, such as linked lists, stacks, queues, heaps, trees, and hash tables, along with their implementation and analysis. The notes also cover various algorithm design techniques such as greedy al...

Homework 1 solutions CS229T/STATS231 (Fall) 1. Value of labeled data (11 points) In many applications, labeled data is expensive and therefore limited, while unlabeled data is cheap and therefore abundant. For example, there are tons of images on the web, but getting labeled images is much harder. What is the statistical value of having labeled data versus unlabeled data? This problem will explore this formally using asymptotics. Specifically, suppose we have an exponential family mod...

Homework 1 solutions CS229T/STATS231 (Fall) 1. Value of labeled data (11 points) In many applications, labeled data is expensive and therefore limited, while unlabeled data is cheap and therefore abundant. For example, there are tons of images on the web, but getting labeled images is much harder. What is the statistical value of having labeled data versus unlabeled data? This problem will explore this formally using asymptotics. Specifically, suppose we have an exponential family mod...

Homework 1 solutions CS229T/STATS231 (Fall) 1. Value of labeled data (11 points) In many applications, labeled data is expensive and therefore limited, while unlabeled data is cheap and therefore abundant. For example, there are tons of images on the web, but getting labeled images is much harder. What is the statistical value of having labeled data versus unlabeled data? This problem will explore this formally using asymptotics. Specifically, suppose we have an exponential family mod...

Concise collection of assignment questions on following topics in Quantum Mechanics-I. FAILURE OF CLASSICAL PHYSICS - blackbody radiation, atomic spectroscopy, photoelectric effect, Compton effect, de Broglie’s hypothesis, Davisson-Germer experiment, double slit experiment. WAVE STATE - Born interpretation and probability density, linear superposition, uncertainty principle, time-dependent and time-independent Schrodinger equation, free particle Gaussian, wavepackets and Fourier transform. ...

DS&A Keyword List. Patel Jill Name as many keywords as possible describing your Data Structures & Algorithms document. Data Structures Algorithms Complexity analysis Array Linked List Stack Queue Tree Graph Hash Table Sorting algorithms Searching algorithms Recursion Dynamic programming Greedy algorithms Divide and conquer Binary search Depth-first search Breadth-first search Heaps AVL Trees Red-Black Trees B-trees Trie Segment Tree Fenwick Tree Dijkstra's alg...

This is introduction of Data structure and algorithms and explains Time Complexity and Big O Notation ,Asymptotic Notations: Big O, Big Omega and Big Theta Explained