Ppt on design and analysis of algorithms

Pointer Analysis – Part II CS 6340. 1 Unification vs. Inclusion Earlier scalable pointer analysis was context- insensitive unification-based [Steensgaard.

with BDDs and a clever context numbering scheme –Inclusion-based pointer analysis 10 14 contexts, 19 minutes –Generates all answers 13 Contribution (2) BDD hacking is complicated  bddbddb (BDD-based deductive database) Pointer analysis in 6 lines of Datalog Automatic / Java projects on SourceForge –Real programs with 100K+ users each Using automatic bddbddb solver –Each analysis only a few lines of code –Easy to try new algorithms, new queries Test system: –Pentium 4 2.2GHz, 1GB RAM –RedHat Fedora Core 1,/


Mantid Manipulation and Analysis Toolkit for ISIS data.

and Analysis Toolkit for ISIS data Agenda TimeItem 2:00Introduction 2:15Python Overview and Running Algorithms 2:30Exercise 1: Removing the Prompt pulse 3:00Generating and generalising scripts 3:10Exercise 2: Create a reusable script 3:40Manipulating graphs and//URD.doc Architectural Design Document –http://svn.mantidproject.org/mantid/trunk/Documents/Design/Architecture%20Des ign%20Document.dochttp://svn.mantidproject.org/mantid/trunk/Documents/Design/Architecture%20Des ign%/of the line and add “;c:python25”


MULTISCALE COMPUTATIONAL METHODS Achi Brandt The Weizmann Institute of Science UCLA www.wisdom.weizmann.ac.il/~achi.

modes Gauge topology: Dirac eq. Inverse problems Optimal design Integral equations Statistical mechanics Massive parallel processing *Rigorous quantitative analysis (1986) FAS (1975) Within one solver (1977,1982) interpolation (order l+p) to a new grid interpolation (order m) of corrections relaxation sweeps algebraic error < truncation error residual transfer enough sweeps or direct solver * *** Full MultiGrid (FMG) algorithm... * h0h0 h 0 /2 h 0 /4/


Randomized Algorithms Randomized Algorithms CS648 1.

develop a useful insight into recurrences. This insight will help you fine-tune the previous inefficient algorithm and eventually lead to design (and analysis) of a more efficient algorithm for min-cut. 16 Common recurrences 17 Common recurrences 18 FASTER MIN-CUT ALGORITHM 19 Revisiting algorithm for min-cut 20 We shall modify this algorithm to improve its success probability. But we shall not allow any significant blow up in/


Design and Analysis of Algorithms Heapsort Haidong Xue Summer 2012, at GSU.

Design and Analysis of Algorithms Heapsort Haidong Xue Summer 2012, at GSU Max-Heap A complete binary tree, and … Yes No every level is completely filled, except possibly the last, which is filled from left to right Max-Heap /. From the last element to the second{ exchange (current, root); l--; Max-Heapify(A, root, l); } Let’s try it Analysis of Heapsort Input: array A Output: sorted array A Algorithm: 1. Build-Max-Heap(A) 2. Last node index l = A’s last node index 3. From the last element to the second/


INTRODUCTION TO CS16 CS16: Introduction to Algorithms and Data Structures Tu/Th 10:30-11:50 Metcalf Auditorium David Laidlaw Thursday, January 23, 2014.

Learn fundamental algorithms and data structures Find and design new ones Reason about them Use them Prepare you for more CS Lectures 10 Homeworks (30%) 4 Projects (30%) 2 Exams (30%) Sections (10%) req’d! Keep up with website! Reading: Dasgupta and readings on blog What are we learning aboot? Basics: Big-O, Recurrence, Mathematical Induction, Hashing Methods: Greedy Algorithms, Divide and Conquer, Dynamic Programming Analysis of Algorithms: Time Complexity/


Algorithms and Data types: Introduction Dr. Andrew Wallace PhD BEng(hons) EurIng

ibn Mūsā al-Khwārizmī Brahmagupta 0628 Algoritmi de numero Indorum. Anthyphairesis Euclidean algorithm Euclids Elements -0299 Gottfried Wilhelm von Leibniz Calculus ratiocinator 1680 Augusta Ada King, Countess of Lovelace 19 th Cent. Algorithms Time and space complexity Analysis Pseudocode Design Data Types What is a data type? A way of classifying pieces of information Useful for computers Examples Primitive Integers, reals, boolean(?) Composite Arrays, struct, unions/


Analysis of Algorithms

Software Challenge: The Big Picture The Software Challenge: Create even larger more efficient algorithms: Design & Analysis of Algorithms Create programs that meet design specs: Program Verification Create readable & understandable programs: Program Documentation Create platform independent programs: Portability The Big Picture Shudder !!! To be and to stay competitive, we need learn to “think ahead of the curve” This means continually learning how to use more sophisticated approaches to the/


UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Fall, 2001 Lecture 1 (Part 1) Introduction/Overview Tuesday, 9/4/01.

Trade ä Algorithm Design Patterns ä dynamic programming, greedy, approximation algorithms ä Advanced Analysis Techniques ä asymptotic analysis ä Theoretical Computer Science principles ä NP-completeness, hardness ä Advanced Data Structures ä interval trees, binomial heaps Asymptotic Growth of Functions Summations Recurrences Sets Probability MATH Proofs Calculus Combinations Logarithms Number Theory Geometry Trigonometry Complex Numbers Permutations Linear Algebra Polynomials Prerequisites ä 91.500 and 91/


International Conference on Computer-Aided Design San Jose, CA Nov. 2001ER UCLA UCLA 1 Congestion Reduction During Placement Based on Integer Programming.

Estimation at late placement stages using probabilistic analysis Mayrhofer and Lauther, ICCAD’90. Partitioning based method Cheng, ICCAD/and double expansion Comparison between single expansion and double expansion circuit: ibm02 International Conference on Computer-Aided Design San Jose, CA Nov. 2001ER UCLA UCLA 22SummarySummary Congestion reduction as a post-processing ILP based congestion reduction control Approximation algorithms with good bound Future Work Extend the approach using ILP instead of/


UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Fall, 2002 Lecture 1 (Part 1) Introduction/Overview Tuesday, 9/3/02.

UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Fall, 2002 Lecture 1 (Part 1) Introduction/Overview Tuesday, 9/3/02 Web Page http://www.cs.uml.edu/~kdaniels/courses/ALG_503.html Web Page Nature of the Course ä Core course: required for all CS graduate students ä Advanced algorithms ä Builds on undergraduate algorithms 91.404 ä No programming required ä “Pencil-and-paper” exercises ä Lectures supplemented by: ä Programs/


UMass Lowell Computer Science 91.504 Advanced Algorithms Computational Geometry Prof. Karen Daniels Spring, 2007 Lecture 1 Course Introduction.

) Common Computational Geometry Structures Voronoi Diagram Convex Hull New Point source: O’Rourke, Computational Geometry in C Delaunay Triangulation Sample Tools of the Trade Algorithm Design Patterns/Techniques: binary searchdivide-and-conquerduality randomizationsweep-line derandomizationparallelism Algorithm Analysis Techniques: asymptotic analysis, amortized analysis Data Structures: winged-edge, quad-edge, range tree, kd-tree Theoretical Computer Science principles: NP-completeness, hardness Growth/


UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Fall, 2004 Lecture 1 (Part 1) Introduction/Overview Wednesday, 9/8/04.

UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Fall, 2004 Lecture 1 (Part 1) Introduction/Overview Wednesday, 9/8/04 Web Page http://www.cs.uml.edu/~kdaniels/courses/ALG_503.html Nature of the Course ä Core course: required for all CS graduate students ä Advanced algorithms ä Builds on undergraduate algorithms 91.404 ä No programming required ä “Pencil-and-paper” exercises ä Lectures supplemented by: ä Programs ä Real-world/


UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Spring, 2002 Lecture 1 (Part 1) Introduction/Overview Tuesday, 1/29/02.

UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Spring, 2002 Lecture 1 (Part 1) Introduction/Overview Tuesday, 1/29/02 Web Page http://www.cs.uml.edu/~kdaniels/courses/ALG_503.html Web Page Nature of the Course ä Core course: required for all CS graduate students ä Advanced algorithms ä Builds on undergraduate algorithms 91.404 ä No programming required ä “Pencil-and-paper” exercises ä Lectures supplemented by: ä Programs/


From Algorithms to Systems-on-a-Chip in a Semester E225C - 2000 Borivoje Nikolić.

phases: –Block design –Putting a system together Simulink /VHDL Simulation Design Projects –/ decoder –SVD System Integration and Simulation Students: Hayun Tang/Design a 1.6 Mbps DSSS timing recovery unit Modulation –Length 31 PN code –QPSK symbol constellation System specifications –Maximum frequency offset of +/- 200 KHz –Minimum input SNR of/and Q paths Need 60 dB IRR Adaptation via /and/algorithm plus soft output Reliability Measure Unit /algorithm: forward and/design flow What did we learn? /


1 GCD The Design and Analysis of Computer Algorithms 8.8 Greatest common divisors and Euclid’s algorithm 報告者:張書豪.

1 GCD The Design and Analysis of Computer Algorithms 8.8 Greatest common divisors and Euclid’s algorithm 報告者:張書豪 2 Outline Half GCD Example 3 HGCD Algorithm procedure HGCD(a 0,a 1 ) : If DEG(a 1 ) ≦ DEG(a 0 )/2 then else begin let a 0 =b 0 x m +c 0, where m= and DEG(c 0 ) < m ; let a 1 = b 1 x m + c 1,where DEG(c 1 ) DEG(3)/2 下限 →b 0 =(4x 2 -7x+11)x+22 b 1 =(-3/16x-93/16)x-45/8 DEG(1)=DEG(2)/2


1 Design and Analysis of Algorithms تصميم وتحليل الخوارزميات (311 عال) Chapter 1 Introduction to Algorithms.

1 Design and Analysis of Algorithms تصميم وتحليل الخوارزميات (311 عال) Chapter 1 Introduction to Algorithms 2 Computational problems A computational problem specifies an input-output relationship  What does the input look like?  What should the output be for each input? Example 1:  Input: an integer number n  Output: Is the number odd ( هل الرقم فردي أو لا )? true Example 2:  Input: array of numbers  Output: the minimum number in the array Example 3:  Input: array of numbers/


Design and Analysis of Algorithms Non-comparison sort (sorting in linear time) Haidong Xue Summer 2012, at GSU.

Design and Analysis of Algorithms Non-comparison sort (sorting in linear time) Haidong Xue Summer 2012, at GSU Comparison based sorting Algorithms that determine sorted order based only on comparisons between the input elements AlgorithmWorst TimeExpected TimeExtra MemoryStable Insertion sortO(1) (in place)Can be Merge sortO(n)Can be Quick sortO(/


Search-based Optimization of Cyber-Physical System Software Deployment & Configuration Dr. Christopher Jules White

design of a large- scale avionics system Improved deployment design reduces: Hardware cost Power consumption Fuel consumption Network load Evolutionary algorithms /and Douglas C. Schmidt, Deployment Automation with BLITZ, 31st International Conference on Software Engineering, May 16-24, 2009 Vancouver, Canada 10.Brian Dougherty, Jules White, Chris Thompson, & Douglas C. Schmidt, Automating Hardware & Software Evolution Analysis, 16th Annual IEEE International Conference & Workshop on the Engineering of/


Chapter 14: Recursion J ava P rogramming: From Problem Analysis to Program Design, From Problem Analysis to Program Design, Second Edition Second Edition.

14: Recursion J ava P rogramming: From Problem Analysis to Program Design, From Problem Analysis to Program Design, Second Edition Second Edition Java Programming: From Problem Analysis to Program Design, Second Edition2 Chapter Objectives  Learn about recursive definitions.  Explore the base case and the general case of a recursive definition.  Learn about recursive algorithms.  Learn about recursive methods.  Become aware of direct and indirect recursion.  Explore how to use recursive methods/


Defining Procedures for Decision Analysis May 02-14 & Engr 466-02A April 30, 2002 Client & Faculty Advisors –Dr. Keith Adams –Dr. John Lamont –Dr. Ralph.

–Limited understanding of algorithms Design Objectives Intended Users & Uses Intended Users & Uses –People in decision-making positions Gain greater understanding of methods Gain greater understanding of methods –Software Programmers Have background reference information Have background reference information Detailed starting point for developing software Detailed starting point for developing software End Product Description The report will aid individuals in conducting a thorough analysis of the decision/


Constraint-Based Embedded Program Composition IMPACT Rapid Construction of Efficient Embedded Systems. Multiple System Variants for Little Cost. Rapid,

Analysis of Models (Design) –Design-Space Exploration: Optimize design, select best configurations from alternative designs Highly scalable using OBDD –Numerical/Algorithmic Simulation with Matlab –Multiple-Resolution Performance Simulation with Discrete Event Simulator Model-Integrated Design /Pr2.assignees =(P1 i or P2 j ))and(Pr2=Pr2 j ) (D1.time - D2.time) < 2 Timing Constraints Constraint Modeling Power Constraints (mode=S2 implies (Proc.Powr<10)) Design Space Exploration Behavior Mod. (Hier. Par/


Starting Work on the MIF Analysis Document Hui Deng, China Mobile Margaret Wasserman, Sandstorm IETF 76, Hiroshima, Japan.

Analysis Document (Starting Now) Purpose of Analysis Do current practices solve the MIF problems? Or are there gaps? –If current practices work, should we standardize them? –If not, can we design/ cases (DHCP, DNS, etc.) –Per prefix configuration for default gateway and routing? Current IETF Standards The standard data model (MIBs, Netconf schema,/merge info from multiple servers Current IETF Standards (cont) Address selection algorithm –MISSING: Policy support, per prefix selection Sockets API –Override /


A Case for Unlimited Watchpoints Joseph L. Greathouse †, Hongyi Xin*, Yixin Luo †‡, Todd Austin † † University of Michigan ‡ Shanghai Jiao Tong University.

and PARSEC Comparing only shadow value checks 14 Watchpoint-Based Taint Analysis 15 19x10x30x207x423x23x 1429x 20% Slowdown 128 entry Range Cache The Need for Many Small Ranges Some watchpoints better suited for ranges  32b Addresses: 2 ranges x 64b each = 16B Some need large # of/generic mechanism Numerous SW systems can utilize a well- designed WP system In the future:  Clear microarchitectural analysis  More software systems, different algorithms 19 20 Thank You BACKUP SLIDES 21 Existing Watchpoint /


STUDENT RESEARCH PROJECTS IN SYSTEM DESIGN Inst. for Information Transmission Problems Russian Academy of Sciences, Moscow 127994, Russia

.”Design of Systems: structural approach”, 2004…2008, Moscow Inst. of Physics & Technology (State Univ.), Students: IT & Cybernetics (about 350 students), advanced undergrad., in Russian & English (individual / team research projects: applications/real world problems, models, algorithms) SOME MY FUNDAMENTALS 1.Learning at 4 levels (from AI) 2.Scale of novelty (creation) by Altshuller (TRIZ) 3.Decision cycle (and corresponding educational flow) 4.Multi-problem support approach (selection/


PORTABLE TEXT TO BRAILLE READER ECE 445 Senior Design: Project #23 David Kim Thung Han Hee Ryan Lee.

1cm2cm 3cm4cm5cm Camera Testing  Resolution Test 80x64128x96160x128320x240 Camera Testing  Lighting Test Camera Testing  Image analysis  After 3V, we see little variation within histogram Meridian/P Microcontroller  C# and.NET Micro Framework  ARM9 Processor at 100 MHz  27 GPIO pins at 3.3 V Software Architecture  Microcontroller Flowchart OCR Algorithm 1. Line Recognition 2. Matrix Mapping 3. Weight Multiplication Initial Solenoid Testing S-10/


Lecture 8 CSE 331. Main Steps in Algorithm Design Problem Statement Algorithm Problem Definition “Implementation” Analysis n! Correctness+Runtime Analysis.

331 Main Steps in Algorithm Design Problem Statement Algorithm Problem Definition “Implementation” Analysis n! Correctness+Runtime Analysis Data Structures Where do graphs fit in? Problem Statement Algorithm Problem Definition Implementation Analysis A tool to define problems Rest of the course Problem Statement Algorithm Problem Definition Implementation Analysis Three general techniques Now: Greedy Algorithms Later: Divide and Conquer Later: Dynamic Programming Greedy algorithms Build the final solution/


Jian Gui WANG Bragg Institute Meeting Java Algorithm Library Dec 14 2006 Java DRA Algorithm Library For Opal Neutron Scattering Data Analysis Team Jian.

2006 2 Outline Introduction Data Reduction and analysis Library Design for DRA Global View of DRA Library Class UML Diagram and Control Summery Jian Gui WANG Bragg Institute Meeting Java Algorithm Library Dec 14 2006 3 Introduction  Provide data reduction capability and support for data analysis tools to the users of the OPAL neutron beam instruments  DRA library contains data reduction and analysis algorithm modules  Provide graphic and non-graphic access interface  Java/


Algorithmic Foundations COMP108 COMP108 Algorithmic Foundations Mathematical Induction Prudence Wong

of Induction  Able to prove by Induction Algorithmic Foundations COMP108 4 (Induction) Analysis of Algorithms After designing an algorithm, we analyze it.  Proof of correctness: show that the algorithm gives the desired result  Time complexity analysis: find out how fast the algorithm runs  Space complexity analysis: find out how much memory space the algorithm requires  Look for improvement: can we improve the algorithm/and so on...... By principle of induction: holds for all +ve integers n Algorithmic/


Created by BM|DESIGN|ER Algo startup Algorythmic Trading Low-Profile Homebroker.

Systems Integration Market Analysis UI Gamification UX Analysis Quant Analysis RELATIONSHIPS Sales Force driven (door to door) Communicate Financial and Technological Expertise Reliable online/and Commission? Licensing Autotrader and Comission? Consultancy in Strategies for Autotrader Strategies Implementation for Autotrader Order Management System Licensing Created by BM|DESIGN|ER PARTNERS Brokerage, Asset Management Softwarehouse VALUE PROPOSITION Inovation because we develop better Algorithms because of/


2014 년 봄학기 강원대학교 컴퓨터과학전공 문양세 이산수학 (Discrete Mathematics)  알고리즘의 복잡도 (Algorithm Complexity)

:= 2 to nt 2 if a i > v then v := a i t 3 return vt 4 Times for each execution of each line. ( 각 line 을 하나의 수행으로 볼 때의 시간 ) Algorithm Complexity Discrete Mathematics by Yang-Sae Moon Page 6 Complexity Analysis of Max Algorithm (2/2) Worst case execution time: procedure max(a 1, a 2, …, a n : integers) v := a 1/100 ns16 m 40 s 2n2n 1.024  s 10 301,004.5 Gyr n!n!3.63 msOuch! You should carefully design algorithms and write programs! Algorithm Complexity Discrete Mathematics by Yang-Sae Moon Page 19 Homework #3/


Music-Inspired Optimization Algorithm Harmony Search

Zong Woo Geem What is Optimization? Procedure to make a system or design as effective, especially the mathematical techniques involved. ( Meta-Heuristics) Finding Best Solution Minimal Cost (Design) Minimal Error (Parameter Calibration) Maximal Profit (Management) Maximal Utility (Economics) Types of Optimization Algorithms Mathematical Algorithms Simplex (LP), BFGS (NLP), B&B (DP) Drawbacks of Mathematical Algorithms LP: Too Ideal (All Linear Functions) NLP: Not for Discrete Var. or/


Institute for Software Integrated Systems Vanderbilt University Design Environment for Fault- Adaptive Systems Ted Bapty Sandeep Neema Sweta Shetty, Steve.

RISC Region Operations Mgr Region Fault Mgr Runtime Design and Analysis Reconfig Behavior Algorithm Fault Behavior Resource Synthesis Performance Simulation Diagnosability Analysis Reliability Analysis System Models Soft Real-Time Hard Experiment Interface /, marks source memory bank as unused/unavail –GET_LOCAL_STATUS Function: Reports status of a resource on a local node –SEND_MESSAGE –RECEIVE_MESSAGE –... Synthesis: Analysis/Offline Simulation –Functional (e.g. Matlab) –Performance (Timing, Discrete Event/


Heapsort O(n lg n) worst case Another design paradigm –Use of a data structure (heap) to manage information during execution of algorithm Comparision-based.

case Another design paradigm –Use of a data structure (heap) to manage information during execution of algorithm Comparision-based Sorting Algorithm Analysis of Algorithms1 Heap Data Structure Analysis of Algorithms2 Heap Property Analysis of Algorithms3 A Heap Example Analysis of Algorithms4 Heap Data Structure Analysis of Algorithms5 Heap Operations Analysis of Algorithms6 Heap Operations Analysis of Algorithms7 Maintaining Heap Analysis of Algorithms8 Runtime Analysis of HEAPIFY Analysis of Algorithms9/


CSC 282: Design & Analysis of Efficient Algorithms Graph Algorithms Shortest Path Algorithms Fall 2013.

Design & Analysis of Efficient Algorithms Graph Algorithms Shortest Path Algorithms Fall 2013 Path Finding Problems Many problems in computer science correspond to searching for paths in a graph from a given start node Route planning Packet-switching VLSI layout 6-degrees of/2002)  Invented concepts of structured programming, synchronization, weakest precondition, and "semaphores" for controlling computer processes. The Oxford English Dictionary cites his use of the words "vector" and "stack" in a computing/


Team 18: Design Optimization of a Supersonic Nozzle

Temperature Density Speed Shock wave development inside nozzle Difference of inlet stagnation pressure and exit pressure Applications Rocket Propulsion Wind Tunnel http://tfm.usc/of nozzle parameters Response Surface Evolutionary Based Algorithm Particle Swarm (PS) Optimal Solution Manufacturing Dimensional Analysis (small scale) True scale versus model Plexiglas design Alternative Materials being considered Relevant Standards AS 9100 Quality management of aerospace industry Created by SAE – Society of/


GATE-540 1 3D Geometric Modeling and Processing (GATE-540) Dr.Çağatay ÜNDEĞER Instructor Middle East Technical University, GameTechnologies & General Manager.

No high-level geometric primitives Incomplete, invalid, conflicting GATE-540 4 Course Objective Develop algorithms for processing and analysis of 3D shapes/geometries How can we make a 3D data/model be usable in your application? GATE-540 5 3D Applications 3D Data can be employed in many domains such as: –Design / Engineering –Health –Security –Training –Education –Entertainment –E-commerce –... GATE-540 6 3D Applications/


UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Spring, 2006 Lecture 2 Monday, 2/6/06 Design Patterns for Optimization.

Analysis of Algorithms Prof. Karen Daniels Spring, 2006 Lecture 2 Monday, 2/6/06 Design Patterns for Optimization Problems Greedy Algorithms Algorithmic Paradigm Context Subproblem solution order Make choice, then solve subproblem(s) Solve subproblem(s), then make choice Greedy Algorithms What is a Greedy Algorithm/ 91.503 textbook Cormen, et al. Running time? Greedy Algorithm ä Algorithm: ä S’ = presort activities in S by nondecreasing finish time ä and renumber ä GREEDY-ACTIVITY-SELECTOR(S’) ä n length[S’]/


Network Design Adam Meyerson Carnegie-Mellon University.

Better constant approximations Online Network Design Demand points arrive one at a time General case as hard as online Steiner tree Tree embedding algorithm is online Access Network Online Special case of single sink and function Simple algorithm: choose cable randomly O(1/) v.s. random order inputs O(log k) v.s. adversarial order inputs Can we do better? Better analysis? /


UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Fall, 2006 Lecture 2 Monday, 9/13/06 Design Patterns for Optimization Problems.

Analysis of Algorithms Prof. Karen Daniels Fall, 2006 Lecture 2 Monday, 9/13/06 Design Patterns for Optimization Problems Greedy Algorithms Algorithmic Paradigm Context Subproblem solution order Make choice, then solve subproblem(s) Solve subproblem(s), then make choice Greedy Algorithms What is a Greedy Algorithm/100$120 Each item has value and weight. Goal: maximize total value of items chosen, subject to weight limit. 0-1: take all or none of an item fractional: can take part of an item source: web site /


1/25 Context-Bounded Analysis of Concurrent Queue Systems Gennaro Parlato University of Illinois at Urbana-Champaign Università degli Studi di Salerno.

of multithreaded programs (Musuvathi-Qadeer, PLDI’07) CHESS at MSR  Context-bounded analysis for otherwise intractable systems Reachability Analysis of Multithreaded Software with Asynchronous Communication (Bouajjani-Esparza-Kiefer-Schwoon, FSTTCS’05) Context-Bounded Analysis of/of a context-switch (p,q)  (p’,q’) Reverse stack q Reverse stack q’ 13/25 Proof (recursive case) Simulate incoming queue and/ the undirected underlying graph is a forest  Algorithm 1.Reverse edges 2.Solvable using bounded context-/


1 Robust Rate Adaptation in 802.11 Networks Starsky H.Y, Hao Yang, Songwu Lu and Vaduvur Bharghavan Presented by Meganne Atkins.

AARF SampleRate –Consecutive successes/losses ARF AARF Hybrid Algorithm –Physical Layer metrics Hybrid Algorithm RBAR OAR –Long-term statistics ONOE Commercially Deployed: ARF, SampleRate and ONOE 6 Issues with Current Algorithms Current Metrics are limited in scope Simulations do not show flaws in the algorithms Performance loss 802.11 non-compliant algorithms –RBAR Flawed design guidelines = Flawed algorithms 7 Current Design Guidelines 1. Decrease Transmission Rate upon severe packet/


03/04/2005ENEE408G Spring 2005 Multimedia Signal Processing 1 ENEE408G: Capstone Design Project: Multimedia Signal Processing Design Project 3: Digital.

Time Domain Spectrogram Pitch and Formant Tracking LPC Spectra Record your own voice and analyze pitch and formants. 03/04/2005ENEE408G Spring 2005 Multimedia Signal Processing 7 Part I. Speech Analysis (4) 03/04/2005ENEE408G Spring 2005 Multimedia Signal Processing 8 Part I. Speech Analysis (5) Gender Identification: Use Auditory Toolbox to obtain Linear Predictive coefficients. Design your algorithm to identify the gender of samples in the training/


Lecture 7 CSE 331 Sep 16, 2009. Feedback forms VOLUNTARY Last 5 mins of the lecture.

of case analysis Did w’ propose to m? Did m accept w’ proposal? 4simpsons.wordpress.com Questions? Extensions Fairness of the GS algorithm Different executions of the GS algorithm Main Steps in Algorithm Design Problem Statement Algorithm Problem Definition “Implementation” Analysis n! Correctness Analysis Definition of Efficiency An algorithm/Read Sec 1.2 and 2.1 in [KT] Asymptotic Analysis (http://xkcd.com/399/) Travelling Salesman Problem Which one is better? Now? And now? The actual run times n/


Algorithms Andrej Bogdanov The Chinese University of Hong Kong and randomness ITCSC Winter School 2010.

algorithm Challenge:Design an algorithm that always splits at least 50% of the edges Method: For each vertex, decide at random if it is red or green If fewer than half the edges are cut, repeat the experiment Algorithm analysis Algorithm finishes in O(m) trials w.p. 99% Analysis: X = number of/(v b )] = Pr z [ ≠ ] deterministic max-cut via “pseudorandom sources” Analysis: Pr[X e = 1] = Pr z [ ≠ ] let’s look at the binary strings for v a and v b v b = 0010101 v a = 0011001 they must differ in some position /


UMass Lowell Computer Science 91.503 Analysis of Algorithms Prof. Karen Daniels Fall, 2008 Lecture 2 Tuesday, 9/16/08 Design Patterns for Optimization.

Analysis of Algorithms Prof. Karen Daniels Fall, 2008 Lecture 2 Tuesday, 9/16/08 Design Patterns for Optimization Problems Greedy Algorithms Algorithmic Paradigm Context Subproblem solution order Make choice, then solve subproblem(s) Solve subproblem(s), then make choice Greedy Algorithms What is a Greedy Algorithm/ 91.503 textbook Cormen, et al. Running time? Greedy Algorithm ä Algorithm: ä S’ = presort activities in S by nondecreasing finish time ä and renumber ä GREEDY-ACTIVITY-SELECTOR(S’) ä n length[S’]/


09/09/2005ENEE408G Fall 2005 Multimedia Signal Processing 1 ENEE408G: Capstone Design Project: Multimedia Signal Processing Design Project 1: Digital Speech.

Time Domain Spectrogram Pitch and Formant Tracking LPC Spectra Record your own voice and analyze pitch and formants. 09/09/2005ENEE408G Fall 2005 Multimedia Signal Processing 7 Part I. Speech Analysis (4) 09/09/2005ENEE408G Fall 2005 Multimedia Signal Processing 8 Part I. Speech Analysis (5) Gender Identification: Use Auditory Toolbox to obtain Linear Predictive coefficients. Design your algorithm to identify the gender of samples in the training/


Approximation Algorithms: Bristol Summer School 2008 Seffi Naor Computer Science Dept. Technion Haifa, Israel TexPoint fonts used in EMF. Read the TexPoint.

design – the primal-dual method 3.Network design – iterative rounding and iterative relaxation 4.Competitive analysis via the primal-dial method Outline 1.Basics 2.Network design – the primal-dual method 3.Network design – iterative rounding and iterative relaxation 4.Competitive analysis via the primal-dial method. What is an Approximation Algorithm/factor be improved? Probably not … Linear Programming Optimize a linear function over a set of linear constraints: minimize c ¢ x subject to: Ax ¸ 0 X ¸ 0 /


Analysis and Design of Algorithms An algorithm is a method of solving problem (on a computer) Problem example: –given a set of points on the plane –find.

Analysis and Design of Algorithms An algorithm is a method of solving problem (on a computer) Problem example: –given a set of points on the plane –find the closest pair Algorithm: –find distance between all pairs Can we do it faster? Combinatorial Problems Closest pair –O(n^2) algorithm TSP –O(n!) algorithm –too slow –difficult problem Course Overview General algorithmic methods –divide and conquer, greedy algorithms, dynamic programming Data structures –hashing, priority queues, binary/


Lecture II : Security Analysis and Planning Internet Security: Principles & Practices John K. Zao, PhD SMIEEE National Chiao-Tung University Fall 2005.

Lecture II : Security Analysis and Planning Internet Security: Principles & Practices John K. Zao, PhD SMIEEE National Chiao-Tung University Fall 2005 2 Internet Security - System Analysis & Planning Theme Objectives  Highlight objectives of security system design & implementation  Introduce procedure of security system planning & operationMotto  Security/Safety is a relative measure  NO system is absolutely secure !  Users’ sense of security is usually a fuzzy warm feeling  Security specialists must /


Ads by Google