PPT-Heuristics for the O-1 Min-Knapsack Problem
Author : karlyn-bohler | Published Date : 2016-05-20
By Farnoosh Davoodi 1 Agenda Min Knapsack Problem 2 approximation greedy algorithm Proof 32 approximation greedy algorithm Proof Another improved heuristic Heuristics
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Heuristics for the O-1 Min-Knapsack Problem: Transcript
By Farnoosh Davoodi 1 Agenda Min Knapsack Problem 2 approximation greedy algorithm Proof 32 approximation greedy algorithm Proof Another improved heuristic Heuristics for the O1 MinKnapsack Problem. 99 4069 3629 3489 3339 3199 3159 3289 3429 3569 3969 4379 50 M Free 14309 13219 12119 11749 11389 11019 10999 11339 11689 12039 13089 14129 100 M Free 34849 32339 25839 25009 24169 23339 23039 23749 24469 25189 31329 33479 200 M Free 73729 65159 6058 2 02 02 02 Flash point TOC F T 79 100 150 150 150 Distillation test T 78 Distillate percentage by volume of tota l distillate to 680F to 437F 25 10 to 500F 40 70 15 55 35 15 to 600F 75 93 60 87 45 80 15 75 Residue from distillation volume Cue soft triangle Bart Jansen, University of Utrecht. Problem background. Geometrical problem statement. Research. Experimental evaluation of heuristics. Heuristics. Results. Conclusion. Outline. 2. Several types of analysis require accessibility . A dynamic approach. Knapsack Problem. Given a sack, able to hold K kg. Given a list of objects. Each has a weight and a value. Try to pack the object in the sack so that the total value is maximized. 1. Merkle-Hellman Knapsack. Public Key Systems . 2. Merkle-Hellman Knapsack. One of first public key systems. Merkle offered $100 award for breaking singly - iterated knapsack. Singly-iterated Merkle - Hellman KC was broken by Adi Shamir in 1982 . At the CRYPTO ’83 conference, Adleman used an Apple II computer to demonstrate Shamir’s method . A dynamic approach. Knapsack Problem. Given a sack, able to hold . W. . kg. Given a list of objects. Each has a weight and a value. Try to pack the object in the sack so that the total value is maximized. Data Structures and Algorithms for the Identification of. Biological Patterns. Marius . Nicolae. Major Advisor: Prof. . Sanguthevar. . Rajasekaran. Associate Advisors: Prof. Ion . Mandoiu. and Prof. . Part 5. Summary created by. Kirk Scott. 1. This set of overheads corresponds to . the first . portion of section 12.3 in the book. The overheads for Chapter 12 roughly track the topics in the chapter. 6 of . Dasgupta. . et al.. October 20, 2015. 2. Outline. Intro. Counting combinations. 0-1 Knapsack (section 6.4). Longest common subsequence. Later. Bellman-Ford (single source shortest path). Floyd-. Kenny Denmark. Jason Isenhower. Ross Roessler. Background. A* uses heuristics for efficient searching. Initially, heuristics provided by "expert". Challenge is to have program create heuristics. . SYFTET. Göteborgs universitet ska skapa en modern, lättanvänd och . effektiv webbmiljö med fokus på användarnas förväntningar.. 1. ETT UNIVERSITET – EN GEMENSAM WEBB. Innehåll som är intressant för de prioriterade målgrupperna samlas på ett ställe till exempel:. is collaborating with JSTOR to digitize preserve and extend access to7KH3KLHOWDDSSDQhttp//wwwjstororgKDWV3UREOHP6ROYLQJXWKRUVf0LFKDHO0DUWLQH6RXUFH7KH3KLHOWDDSSDQ9RO1RSUfSS3XEOLVKHGE3KLHOWDDSSDQWHUQDWL
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