Algorithm算法代写代考

CSCI 4041 Discussion Section Notes mo000007@umn.edu Expectations and Resources ● Online Textbook: Introduction to Algorithms, Third Edition ● Please watch the lecture videos before the discussion sections! ● We will review what we have learned and do some programming exercises ● Grab a paper or open up any IDE (any of your choice) or Google […]

Greedy algorithms Find the best solution to a local problem and (hope) it solves the global problem Greedy algorithms Greedy algorithms find the global maximum when: 1. optimal substructure – optimal solution to a subproblem is a optimal solution to global problem 2. greedy choices are optimal solutions to subproblems Greedy algorithm A list of

Introduction / Review Number of transistors double every two years This trend has slowed a bit, closer to doubling every 2.5 years Moore’s Law Memory: 1 MB CPU: 2.4 Mhz First computer Do you remember how fast the CPU was on your first computer? How about your current computer? What about your previous computer? CPU

CS 4610/5335 Point-cloud perception Robert Platt Northeastern University Material adapted from: 1. Lawson Wong, CS 4610/5335 2. Peter Corke, Robotics, Vision and Control – Depth sensors – Creating point clouds / maps – Voxelizing, calculating surface normals, denoising – ICP – RANSAC – Hough transform Topics Fake perception https://april.eecs.umich.edu/software/apriltag Incredibly useful and reliable! 3-D vs.

CSCI 4041 Discussion Section Notes mo000007@umn.edu MergeSort Divide and Conquer algorithm ● Recursively divide into subproblems (smaller size) ● Solve each subproblem ● Conquer/combine each solution back How MergeSort works? ● Divide n elements into 2 subproblems, each has n/2 elements ● Recursively sort each subproblem using merge-sort ● Merge each sorted arrays into one

Sorting Sorting! -What’s a sorting algorithm? -Insertion sort -Merge sort -Divide & conquer (Master’s thm) -Quicksort Outline Sorting! -What’s a sorting algorithm? -Insertion sort -Merge sort -Divide & conquer (Master’s thm) -Quicksort Outline Input: sequence of numbers = {a1, a2, … an} Output: different order = {a1′, a2′, … an’}, where a1′ < a2' <

Sorting in O(n) 1 So far we have been looking at comparative sorts (where we only can compute < or >, but have no idea on range of numbers) The minimum running time for this type of algorithm is Θ(n lg n) 3 Sorting! 5 All n! permutations must be leaves Worst case is tree