PPT-1 The Sorting Problem Input:

A sequence of n numbers a 1 a 2 a n Output A permutation reordering a 1 a 2 a n of the input sequence such that a 1 a 2 a. Merge Sort Running time 920 log where is the number of elements to be sorted Apply the Divide Conquer Combine Principle sort 12 12 sort 12 12 split merge P FlenerIT DeptUppsala Univ AD1 FP PK II Sorting B1 brPage 2br Sorting Merge Sort Merging t Insertion Sort. Insertion Sort. Sorting problem:. Given an array of N integers, rearrange them so that they are in increasing order.. Insertion sort. Brute-force sorting solution.. In each iteration . Agents of Deposition. The agents of erosion are also agents of deposition.. Depositional rate depends on sediment size, shape and density.. Depositional rate also depends on characteristics of the depositional agent.. Ron Zeira. and Ron Shamir. Combinatorial Pattern Matching 2015. 30.6.15. G. enome rearrangements. Motivation I: evolution. Human genome project. Motivation II: cancer. NCI, 2001. Normal karyotype. MCF-7 breast cancer cell-line. Proposal – V2. Ugur Akgun. Sorting Proposal. Low . eta – . H. igh . gain. High eta – Low . gain. Low gain . EM (long fiber). High Gain . Hadronic. (short fiber). 36 . Robox. , 13 . t. owers each, . Sorting. . and. . Order. . Statistics. – . Part. II. Lecture . 5. CIS 670. Comp 122. Sorting – Definitions. Input: . n. . records. , . R. 1. … . R. n. , from a . file. .. Each record . R. ADMIT. Tags. Applications . can be tagged using the Tag action from the application toolbar. . Tags. There are several reasons to use a tag for an application:.  . To tag an applicant with a . Coordinator Decision. Onset of . precip. – development of particles large enough to sediment relative to cloud droplets & ice crystals.. Larger particles tend to fall faster.. Differential Sedimentation (D.S.). Atmospheric flows (e.g. updrafts) can prolong D.S. due to the removal of small drops upward & exhausted through the anvil region.. Bubble Sort . of an array. Inefficient --- . O ( N. 2. ). easy to code. , . hence unlikely to contain errors. Algorithm. for . outerloop. = 1 to N. for . innerloop. = 0 to N-2. if ( item[. David Woodruff. Carnegie Mellon University. Theme: Tight Upper and Lower Bounds. Number of comparisons to sort an array. Number of exchanges to sort an array. Number of comparisons needed to find the largest and second-largest elements in an array. Lecture 18 SORTING in Hardware SSEG GPO2 Sorting Switches LED Buttons GPI2 Sorting - Required I nterface Sort Clock R eset n DataIn N DataOut N Done RAdd L WrInit S (0=initialization 1=computations) Θ. (n. 2. ). Merge Sort:. Θ. (. nlog. (n)). Heap Sort:. Θ. (. nlog. (n)). We seem to be stuck at . Θ. (. nlog. (n)). Hypothesis: . Every sorting algorithm requires . Ω. (. nlog. (n)) time.. Lower Bound Definitions. Given. a set (container) of n elements . E.g. array, set of words, etc. . Goal. Arrange the elements in ascending order. Start .  . 1 23 2 56 9 8 10 100. End .  1 2 8 9 10 23 56 100 (Ascending). CS 165: Project in Algorithms . and Data Structures. Michael T. Goodrich. Some slides are from J. Miller, CSE 373, U. Washington. Why Sorting?. Practical application. People by last name. Countries by population.