'Knapsack problem' presentation slideshows

INTRODUCTION TO ALOGORITHM DESIGN STRATEGIES. Subject: design & Analysis of algorithm, CSE-5 th Sem. Prepared By Prof. Saquib Ahmed Anjuman College of Engineering & Technology Department of Computer Science & Engineering. Syllabus.

The Knapsack Problem. The classic Knapsack problem is typically put forth as: A thief breaks into a store and wants to fill his knapsack with as much value in goods as possible before making his escape. Given the following list of items available, what should he take?

Knapsack Problem: Greedy vs. Brute Force. pp 313-317 (Section 7.6). Greedy Approach. To solve problems you have to make decisions. At each decision point, you pick the greedy (or best) option. i.e., make an optimal move given what you know For some problems a greed strategy

Knapsack Problem. Truck – 10t capacity Optimum cargo combination: Item 1: $5 (3t) Item 2: $7 (4t) Item 3: $8 (5t). Knapsack Problem. Output function f(i,w)  Optimum output of a combination of items 1 to i with a cumulated weight of w or less. Item 1: x1=$5 ; w1=3t Item 2: x2=$7 ; w2=4t

CS 3343: Analysis of Algorithms. More Examples on Dynamic Programming. Review of Dynamic Programming. We’ve learned how to use DP to solve a special shortest path problem the longest subsequence problem a general sequence alignment When should I use dynamic programming?

Genetic Algorithm (Knapsack Problem). Anas S. To’meh. Genetic Algorithm. What is Genetic Algorithm?. Follows steps inspired by the biological processes of evolution.

CS10: The Beauty and Joy of Computing Lecture #22 Limits of Computing 2012-04-16. UC Berkeley EECS Lecturer SOE Dan Garcia. Researchers at Facebook and the University of Milan found that the avg # of “friends” separating any two people in the world was < 6.

Public Key Cryptosystem. In Symmetric or Private Key cryptosystems the encryption and decryption keys are either the same or can be easily found from each other.

Lecture 4 – Network Flow Programming. Topics Terminology and Notation Network diagrams Generic problems (TP, AP, SPP, STP, MF) LP formulations Finding solutions with Excel add-in. Network Optimization. Network flow programming (NFP) is a special case of linear programming

P2 project Bag Packer Program. Made by: Mette T. Pedersen Aleksander S. Nilsson Niels B. Pedersen Kasper Plejdrup Christian J. O’Keeffe Rasmus F. Gadensgaard Dag T. B. Pedersen. Content of presentation. Problem Analysis Theory Design Development Testing Discussion & Conclusion

6.006- Introduction to Algorithms. Lecture 20 Prof. Constantinos Daskalakis. Lecture overview. longest common subsequence: the bottom-up approach reconstructing the LCS: back-pointers knapsack. Longest Common Subsequence.

Branch and Bound Algorithm Analysis & Design. Derek DaSilva Bryan Masson. Overview. Branch and Bound General algorithm used for optimization Splits a problem set into two or more smaller sets, i.e. Branching Uses a “bound” to rule out specific solutions to a problem

Dynamic Programming. What is Dynamic Programming. A method for solving complex problems by breaking them down into simpler sub problems. It is applicable to problems exhibiting the properties of overlapping subproblems which are only slightly smaller

MA/CSSE 473 Day 12. Amortization (growable Array) Knuth interview Brute Force Examples. MA/CSSE 473 Day 12. Questions? Donald Knuth Interview Amortization Brute force (if time) Divide and conquer intro. Q1-2. Donald Knuth Interview.

Greedy Algorithms. Greedy Algorithm. Greedy Algorithm - Makes locally optimal choice at each stage. - For optimization problems. If the local optimum is a part of the global optimum, we get the global optimum. Greedy Algorithm vs Dynamic Programming. Dynamic Programming. Greedy

Greedy Algorithms. Greedy Methods ( 描述1 ). 解最佳化問題的演算法 , 其解題過程可看成是由一連串的決策步驟所組成 , 而每一步驟都有一組選擇要選定 . 一個 greedy method 在每一決策步驟總是選定那目前 看來最好 的選擇 . Greedy methods 並不保證總是得到最佳解 , 但在有些問題卻可以得到最佳解 . Greedy Methods ( 描述2 ). Greedy 演算法經常 是非常有效率且簡單的演算 ; 但 但較難證明其正確性 ( 與 DP 演算法比較 ).

CS10 The Beauty and Joy of Computing Lecture #23 : Limits of Computing 2011-11-23. UC Berkeley EECS Lecturer SOE Dan Garcia. Researchers at Facebook and the University of Milan found that the avg # of “friends” separating any two people in the world was < 6. 4.74 degrees of separation?.

Approximate and online multi-issue negotiation. S.S. Fatima Loughborough University, UK S.S.Fatima@lboro.ac.uk M. Wooldridge N.R. Jennings

CSE 571 Advanced Artificial Intelligence. Oct 22, 2003 Class Notes Transcribed By: Jon Lammers. Ch8 – Smodels, DLV, Pure Prolog. Smodels & DLV Compute answer sets and compare to results. Pure Prolog Looks for items in result in the head. Works if you don’t use cut or ordering.

Dynamic Programming (1). Dynamic Programming. This is probably the most challenging topic in the course Dynamic Programming is not a specific problem with a specific solution (like everything else that we’ve done up to now)