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1. Computational Complexity Theory - Wikipedia, The Free Encyclopedia
As a branch of the theory of computation in computer science, computational Complexity theory investigates the problems related to the amounts of resources
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Computational complexity theory
From Wikipedia, the free encyclopedia
Jump to: navigation search Computer science Portal As a branch of the theory of computation in computer science computational complexity theory investigates the problems related to the amounts of resources required for the execution of algorithms (e.g., execution time ), and the inherent difficulty in providing efficient algorithms for specific computational problems A typical question of the theory is, "As the size of the input to an algorithm increases, how do the running time and memory requirements of the algorithm change and what are the implications and ramifications of that change?" In other words, the theory, among other things, investigates the scalability of computational problems and algorithms. In particular, the theory places practical limits on what computers can accomplish. An important aspect of the theory is to categorize computational problems and algorithms into complexity classes . The most important open question of complexity theory is whether the complexity class P is the same as the complexity class NP , or is a strict subset as is generally believed. Shortly after the question was first posed, it was realised that many important industry problems in the field of

2. Notes For 198:538
198538 Complexity of computation. Notes for the Spring, 1998 version of the class are now becoming available. Additional notes will be appearing here.
198:538: Complexity of Computation
Notes for the Spring, 1998 version of the class are now becoming available. Additional notes will be appearing here.

3. Internet Archive: Details: Complexity And Computation Of 3D Delaunay Trangulatio
Complexity and computation of 3D Delaunay trangulations. Speaker Nina Amenta Date October, 2003 This item is part of the collection Math Lectures from
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Complexity and computation of 3D Delaunay trangulations
Speaker: Nina Amenta Date: October, 2003

4. DIMACS Workshop On Intrinsic Complexity Of Computation
DIMACS Workshop on Intrinsic Complexity of computation. April 10 13, 2000 DIMACS Center, Rutgers University, Piscataway, NJ
DIMACS Workshop on Intrinsic Complexity of Computation
April 10 - 13, 2000
DIMACS Center, Rutgers University, Piscataway, NJ
Paul Beame , University of Washington,
Steven Rudich , Carnegie Mellon University,
Andrew Yao , Princeton University,
Presented under the auspices of the Special Year on Computational Intractability
  • Workshop Announcement
  • Call for Participation
  • Program ...
  • Registration Form There is a $40/day registration fee, $5/day for non-DIMACS postdocs, to be collected on site, cash or check only. The fee is waived for graduate students and DIMACS long-term visitors and postdocs in residence at DIMACS. Fees for all members are covered through their institution's membership in DIMACS and therefore no member needs to pay the registration fee.
  • Information on Accommodations
  • Information on Travel Arrangements
  • Parking Permit Parking permits will be available at the registration table on the day of the workshop.
  • Important Reimbursement Information Attendees who have been offered support should keep two rules in mind. Reimbursement for air travel can only be made for travel on

5. APS - 2006 APS March Meeting - Event - Complexity, Parallel Computation And Stat
Abstract Z33.00004 Complexity, Parallel computation and Statistical Physics The talk will review concepts of parallel computational Complexity theory
APS Meetings
2006 APS March Meeting
Session Z33: Statistical and Nonlinear Physics
Baltimore Convention Center - 336
Sponsoring Unit: GSNP
Chair: Kurt Wiesenfeld, Georgia Institute of Technology
Abstract ID: BAPS.2006.MAR.Z33.4
Abstract: Z33.00004 : Complexity, Parallel Computation and Statistical Physics
Preview Abstract
Jonathan Machta
(University of Massachusetts Amherst) To cite this abstract, use the following reference:

6. The Evolving Mind: Chapter 1 - PATTERN, COMPLEXITY, AND COMPUTATION
The KolmogorovChaitin-Solomonoff (KCS) definition of Complexity says roughly that the Complexity of x is the length of the shortest program for computing x
The Evolving Mind Back to The Evolving Mind Table of Contents
Chapter 1:
The argument-structure of this book is a complex one. We shall begin with the evolution of immune systems, then turn to the evolution of species, and only then return to our main topic of interest, evolutionary processes in the mind and brain. But before we can speak about evolution at all, we will need to discuss a few important background ideas, mostly from theoretical computer science. These ideas are not usually mentioned in connection with evolutionary theory. However, I believe that they are absolutely essential to the understanding of the self-organizing processes involved in evolutionary systems. Let us begin with a simple yet crucial ontological axiom, which Gregory Bateson called the "Metapattern." patterns 1.1 A FORMAL THEORY OF PATTERN In order to make practical use of the Metapattern, we will require a better idea of what a pattern actually is.

7. IngentaConnect Towards An Energy Complexity Of Computation*
Towards an energy Complexity of computation*. Author Martin A.J.. Source Information Processing Letters, Volume 77, Number 2, 28 February 2001 , pp.
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8. Optimal Models Of Disjunctive Logic Programs: Semantics, Complexity, And Computa
20 N. Leone, F. Scarcello, and V.S. Subrahmanian, Optimal Models of Disjunctive Logic Programs Semantics, Complexity, and computation Univ. of Maryland
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9. Counting Complexity And Computational Group Theory
of pseudorandom functions and permutations GGM,LR, where efficiency refers both to the sequential and parallel time Complexity of the computation.
PhD Theses Series of ECCC
Reingold, Omer
The Weizmann Institute of Science
Rehovot, Israel, 1999
Pseudo-Random Synthesizers, Functions and Permutations
PostScript (477KB, gzip'ed)
The research reflected in this dissertation is a study of (computational) pseudo-randomness. More specifically, the main objective of this research is the efficient and simple construction of pseudo-random functions and permutations [GGM,LR], where efficiency refers both to the sequential and parallel time complexity of the computation. Pseudo-random functions and permutations are fundamental cryptographic primitives with many applications in cryptography and more generally in computational complexity.
Constructions of Pseudo-Random Functions
For our constructions of pseudo-random functions, we introduce and study a new cryptographic primitive which we call a pseudo-random synthesizer and a generalization of this primitive which we call a k-dimensional pseudo-random synthesizer. These primitives are of independent interest as well. In addition, we consider various applications of our constructions and study some of the underlying cryptographic assumptions used in these constructions. The main results obtained by this research are:
  • Introducing new cryptographic primitives called pseudo-random synthesizer and k-dimensional pseudo-random synthesizer.

10. [cond-mat/0510809] Complexity, Parallel Computation And Statistical Physics
Complexity, parallel computation and statistical physics. Authors J. Machta Comments 21 pages, 7 figures Subjclass Statistical Mechanics cond-mat
Search or Article-id Help Advanced search All papers Titles Authors Abstracts Full text
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Citations p revious n ... ext
Condensed Matter > Statistical Mechanics
Title: Complexity, parallel computation and statistical physics
Authors: J. Machta (Submitted on 29 Oct 2005) Abstract: The intuition that a long history is required for the emergence of complexity in natural systems is formalized using the notion of depth. The depth of a system is defined in terms of the number of parallel computational steps needed to simulate it. Depth provides an objective, irreducible measure of history applicable to systems of the kind studied in statistical physics. It is argued that physical complexity cannot occur in the absence of substantial depth and that depth is a useful proxy for physical complexity. The ideas are illustrated for a variety of systems in statistical physics. Comments: 21 pages, 7 figures Subjects: Statistical Mechanics (cond-mat.stat-mech) Journal reference: Complexity Journal, 11 (5), 46-64 (2006)

11. DBLP: Klaus Meer
4, Klaus Meer On the Complexity of Quadratic Programming in Real Number Models of computation. Theor. Comput. Sci. 133(1) 8584 (1994)
Klaus Meer
List of publications from the DBLP Bibliography Server FAQ Coauthor Index - Ask others: ACM DL Guide CiteSeer CSB ... Marco Gori , Klaus Meer: Some Aspects of a Complexity Theory for Continuous Time Systems. CiE 2007 EE Klaus Meer, Martin Ziegler : Real Computational Universality: The Word Problem for a Class of Groups with Infinite Presentation. MFCS 2007 EE Klaus Meer: Simulated Annealing versus Metropolis for a TSP instance. Inf. Process. Lett. 104 EE Gregorio Malajovich , Klaus Meer: Computing Minimal Multi-Homogeneous Bezout Numbers Is Hard. Theory Comput. Syst. 40 EE Klaus Meer: Some Relations between Approximation Problems and PCPs over the Real Numbers. Theory Comput. Syst. 41 Thomas Lickteig , Klaus Meer, Luis Miguel Pardo : Real Computation and Complexity, 1.-6. February 2004 EE Klaus Meer: Optimization and Approximation Problems Related to Polynomial System Solving. CiE 2006 EE Klaus Meer, Martin Ziegler : Uncomputability Below the Real Halting Problem. CiE 2006 EE Klaus Meer, Dieter Rautenbach : On the OBDD Size for Graphs of Bounded Tree- and Clique-Width. IWPEC 2006 EE Uffe Flarup Hansen , Klaus Meer: Approximation Classes for Real Number Optimization Problems.

12. Proof, Computation, Complexity
WilhelmSchickard-Institut, University of Tübingen, Germany; 89 April 2002.
Proof, Computation, Complexity
International workshop
April 8th and 9th, 2002
List of participants Location Travel information
The workshop is aimed at computer scientists who share an active interest in proof theory, computation and complexity theory.
It focusses on recent developments in these fields, and it strongly supports discussion of perspectives in future research.
Preliminary list of participants
Program commitee:
Birgit Elbl
Reinhard Kahle
Location The workshop will take place in the Fürstenzimmer in the castle of Tübingen
Travel information
We have reserved rooms at the guest house of the university, Lessingweg 3 If you come by car, please consult a map If you come by train, you can use the bus lines

13. On The Complexity Of Computation Of A Pair Of Monomials In Two Variables
We study the generalisation of the problem on efficient computation of the power xn for given x and n (or the equivalent problem on minimal addition chain
Wissenschaft.Online Suche

14. Directory Of Faculty And Staff | National Institute Of Informatics
Addressing the Complexity of computation based on mathematical logic I m interested in and intrigued by this Complexity of computation.
Introduction of researcher
Addressing the complexity of computation based on mathematical logic
Terui Kazushige
Associate Professor, Principles of Informatics Research Division
Doctoral Degrees (Keio University) 2002, Ph.D in Philosophy Research Fields Logic, Theoretical Computer Science
Detail You can see detail from this url

Certain mathematical problems can't be solved even by high-performance computers. It is logically impossible that there exists a ``perfect'' computer capable of solving all mathematical questions. Why is this? Why are there limitations on computation? Why does our world have this sort of imperfection? I'd like to find the answers to these questions by examining the topic of computation based on mathematical logic.
Complexity of computation
The assessment "impossible to compute" can mean two things. It can indicate that the computation is logically impossible or that the calculation is too complex and would take too long to be feasible. Logically impossible computation can't be done simply due to the nature of computers and computing that's been known since Alan Turing pioneered the idea of modern computers.
On the other hand, not being feasible refers to computer limitations with respect to time and space resources. For example, even the most advanced computer can't factor a 300-digit number in less than several trillion years. Since we can't see the results of this calculation, the attempt is more or less pointless, unless a computer based on a completely new concept is developed and becomes available.

15. JSTOR On The Complexity And Computation Of View Graphs Of
On the Complexity and computation of view graphs of piecewise smooth algebraic surfaces BY J. H. RIEGERt FB Informatik, Universitdt Hamburg, VogtKolln-Str.<1899:OTCACO>2.0.CO;

16. On The Time And Space Complexity Of Computation Using Write-Once Memory - OR - I
@techreport{IraniCSD88-434, Author = {Sandy Irani and Moni Naor and Ronitt Rubinfeld}, Title = {On the Time and Space Complexity of computation Using
@import "/Includes/eecsPage.css";
Electrical Engineering and Computer Sciences
UC Berkeley
On the Time and Space Complexity of Computation Using Write-Once Memory - OR - Is Pen Really Much Worse Than Pencil?
Sandy Irani, Moni Naor and Ronitt Rubinfeld
EECS Department
University of California, Berkeley
Technical Report No. UCB/CSD-88-434
BibTeX citation: EndNote citation: %0 Report %A Irani, Sandy %A Naor, Moni %A Rubinfeld, Ronitt %T On the Time and Space Complexity of Computation Using Write-Once Memory - OR - Is Pen Really Much Worse Than Pencil? %I EECS Department, University of California, Berkeley %D 1988 %@ UCB/CSD-88-434 %U %F Irani:CSD-88-434
Contact Webteam

17. Science Links Japan | Complexity Of Computation On ID-based Key Sharing Systems
We also estimate the Complexity of computation for new scheme over the suitable elliptic curves. (author abst.)
Home Opinions Press Releases ... IEIC Technical Report (Institute of Electronics, Information and Communication Engineers)(2000)
Complexity of Computation on ID-based Key Sharing Systems with Pairing over Elliptic Curve.
Accession number; Title; Complexity of Computation on ID-based Key Sharing Systems with Pairing over Elliptic Curve. Author; YAMANAKA TADAKAZU(Kyoto Inst. of Technol., Fac. of Eng. and Des.) OGISHI KIYOSHI(Matsushita Electr. Ind. Co., Ltd.) SAKAI RYUICHI(Osaka Electro-Communication Univ., Fac. of Eng.) KASAHARA MASAO(Osakagakuindai Joho) Journal Title; IEIC Technical Report (Institute of Electronics, Information and Communication Engineers)
Journal Code:
ISSN: VOL. NO. PAGE. TBL.3, REF.10 Pub. Country; Japan Language; Japanese Abstract; Various interesting proposals on ID-based non interactive key sharing shemes have been made so far. However most of the schemes have problems of collusion attacks. New ID-based non interactive key sharing sheme over elliptic curves was proposed by Ohgishi, Sakai and Kasahara in Nov. 1999. This new scheme realizes a higher security against the collusion attacks. In this paper, we present the algorithms of the construction of the suitable elliptic curves for the new scheme. We also estimate the complexity of computation for new scheme over the suitable elliptic curves. (author abst.) BACK About J-EAST How to use List of Publications ... FAQ

18. Keith Price Bibliography Aspect Graphs, Matching Systems
On the Complexity and computation of View Graphs of PiecewiseSmooth Algebraic Surfaces, TRFBI-HH-M-228/93, Universitat Hamburg, 1993. BibRef 9300 Aspect Graphs, Matching Systems
Chapter Contents (Back) Object Recognition Matching, Volumes Aspect Graphs ... Basri, R.
Viewer-Centered Representations in Object Recognition: A Computational Approach
(Chapter V:4). (Massachusetts Inst. Tech) BibRef Seibert, M. , and Waxman, A.M.
Adaptive 3-D Object Recognition from Multiple Views
, No. 2, February 1992, pp. 107-124.
IEEE Abstract
IEEE Top Reference
WWW Version
. Given a sequence of views, accumulate the recognition and transformation parameters. At any time, the highest one is the winner, but it can change as new views are added. Uses unoccluded objects. BibRef Gigus, Z. Canny, J. , and Seidel, R.
Efficiently Computing and Representing Aspect Graphs of Polyhedral Objects
, No. 6, June 1991, pp. 542-551.
IEEE Abstract
IEEE Top Reference
WWW Version
BibRef Earlier: IEEE Abstract IEEE Top Reference . Partition the viewing space and generate representative views of objects. BibRef Laurentini, A. Comments on: Efficiently Computing and Representing Aspect Graphs of Polyhedral Objects PAMI(18) , No. 1, January 1996, pp. 57-58.

19. Rusins Martins Freivalds
The primary area of my research has always been Complexity of computation. In 1975 I proved the very first theorem on advantages of randomized algorithms
Rusins Martins FREIVALDS Professor Rusins Martins FREIVALDS Head of Division of Discrete Mathematics
Faculty of Physics and Mathematics, University of Latvia

Raina bulv. 29
Riga, LV 1459
Latvia Phone: +371 722 6997
Fax: +371 782 0153
E-mail: Born: November 10, 1942, Cesvaine, Latvia Interests:
  • Inductive Inference (Computational Learning Theory) Randomized Algorithms (Complexity of Computation) Mathematical Foundations of Computer Science
Brief Description of Main Research: The primary area of my research has always been complexity of computation. In 1975 I proved the very first theorem on advantages of randomized algorithms over deterministic ones. Namely, I have proven that randomized Turing machines can use less running time than deterministic ones to compute certain functions. Recently I have developed new powerful methods to prove lower bounds for time and space complexity of randomized algorithms. I have published various results in Inductive Inference. I have tried to use deep methods of classical mathematics for problems in Theoretical Computer Science. I would like to mention the usage of constructive ordinals to measure the complexity of Inductive Inference, and the usage of Group Theory in Inductive Inference. Languages: Latvian, Russian, English, German

20. RFCD Classification COMPUTATION THEORY AND MATHEMATICS: Analysis Of Algorithms A
Experts associated with RFCD Classification computation THEORY AND MATHEMATICS Analysis of Algorithms and Complexity (computation THEORY AND MATHEMATICS)
Skip to navigation Skip to content University home page
Find an Expert Profiling the University of Melbourne's Researchers
Links: University Homepage About the University Students Research Community News Events Faculties A-Z Directory Library Search: Home
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Date created:
01 June 2006
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21. Faculty Page
Area Pseudorandomness and Derandomization, Complexity of computation, Automata and Logic Profile Contact Information Home Page
CS SFU Home Education
Computing Science Faculty
Select a faculty profile from this index, or browse the alphabetical listing below for faculty home pages... Professors: Atkins Bhattacharya Burton Cameron ... Wang
Associate Professors: Berenbrink Ergun Ester Hafer ... Wiese
Assistant Professors: Beyer Bulatov Fedorova Hamarneh ... Zhang
Lecturers: Baker Bart Cukierman Dixon ... Regan
M. Stella Atkins, Professor
Area: Medical Computing (includes laparoscopic surgery, sleep studies, and telehealth); Medical Image Display and Analysis
Home Page
Greg Baker, Senior Lecturer
Area: Instruction
Home Page
Brad Bart, Senior Lecturer
Area: Instruction
Home Page
Petra Berenbrink, Associate Professor
Area: Probabilistic methods; Randomized algorithms; Analysis of dynamic processes
Home Page
Dirk Beyer, Assistant Professor
Area: Software Engineering, Software Verification, Structure Analysis of Large Systems
Home Page
Binay Bhattacharya, Professor
Area: Computational Geometry, Pattern Recognition
Home Page
Andrei Bulatov, Assistant Professor

22. What Is The Connection Between Complexity Of Computation And Sub Recursive Hiera
What, if anything, is the connection between Complexity of computation and sub recursive hierarchies? Probably nothing but I can t help
Help Sign in sci.logic Discussions ... Subscribe to this group This is a Usenet group - learn more What is the connection between complexity of computation and sub recursive hierarchies? Options There are currently too many topics in this group that display first. To make this topic appear first, remove this option from another topic. There was an error processing your request. Please try again. Standard view View as tree Proportional text Fixed text messages Collapse all The group you are posting to is a Usenet group . Messages posted to this group will make your email address visible to anyone on the Internet. Your reply message has not been sent. Your post was successful Frederick Williams View profile More options Oct 15, 1:09 am Newsgroups: sci.logic From: Frederick Williams <"Frederick Williams"> Date: Sun, 14 Oct 2007 13:09:26 GMT Local: Mon, Oct 15 2007 1:09 am Subject: What is the connection between complexity of computation and sub recursive hierarchies? Reply to author Forward Print Individual message ... Find messages by this author What, if anything, is the connection between complexity of computation
and sub recursive hierarchies? Probably nothing but I can't help

(4) The Complexity of the brain can be quantified and is shortly to be exceeded by the Complexity of computation as available on humanlybuilt computers.
THE PRIMACY OF THE FIRST PERSON: REPLY TO RAY KURZWEIL By William Dembski I'd like to take this opportunity to thank Micah Sparacio for organizing the discussion of the recently published book Are We Spiritual Machines? as well as Ray Kurzweil for his response to my essay in that book and his willingness to take part in this discussion. My essay in that book was titled "Kurzweil's Impoverished Spirituality" and was essentially a stripped down version of a piece I had done for First Things in which I critiqued both Kurzweil and Nancey Murphy with regard to their materialistic account of human mentality and spirituality. The audience of that periodical would largely have been sympathetic to my case, being generally opposed to materialism. I see now, given Kurzweil's response, that it would have been better to provide a more thorough analysis of his project and a more detailed technical response to his claims. I doubt that it could have been done there with the thoroughness required in the space allotted, and I certainly won't attempt it here. Eventually I will get to it. I want here to outline where our projects diverge and why I find his project unconvincing. Now the predictability of materialism as a philosophical project does not mean that material systems need be predictable. I'm all too aware of

24. Diskretnaya Matematika
On the Complexity of computation in finite Abelian, nilpotent and soluble groups V. V. Kochergin UDC 519.714+512.542 Received 13.12.1991
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Diskr. Mat., 1993, Volume 5 Issue 1 (Mi dm670)
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On the complexity of computation in finite Abelian, nilpotent and soluble groups
V. V. Kochergin

V. V. Kochergin, On the complexity of computation in finite Abelian, nilpotent and soluble groups, Diskr. Mat., 1993, Linking options:
  • Full text (in Russian): PDF file (1792 kB) English version: Discrete Mathematics and Applications, 1993, Review databases: Citing articles on Google Scholar: Russian citations English citations Related articles on Google Scholar: Russian articles English articles Contact us: Steklov Mathematical Institute RAS Branch of Mathematical Sciences, Russian Academy of Sciences
    Institut Henri Poincaré (IHP). January 4 April 7, 2006 Paris, France. Programme. QUANTUM INFORMATION, computation AND Complexity

    26. Logic And Computation Complexity 2005
    The Logic and Computational Complexity Workshop (LCC 05) will be held on 2425 June 2005 in Chicago as a satellite workshop of the Logic in Computer Science
    LCC 2005
    Seventh International Workshop on
    Logic and Computational Complexity
    The Logic and Computational Complexity Workshop (LCC'05) will be held on 24-25 June 2005 in Chicago as a satellite workshop of the Logic in Computer Science Conference (LICS'05). All talks will be in: The 9th Floor Digital Cinema Studio ( ), School of CTI, DePaul University, 243 South Wabash Ave. PROGRAM SESSION 1: FRIDAY JUNE 24. Chair: James Royer INVITED TALK: Higher-Order Matching and Games
    Colin Stirling (University of Edinburgh) Synthesis of Quasi-Interpretations
    Guillaume Bonfante, Jean-Yves Marion, Jean-Yves Moyen, and Romain Pchoux (LORIA/Nancy) coffee break SESSION 2: FRIDAY JUNE 24. Chair: Leonid Libkinr INVITED TALK: Queries on Tree-Structured Data: Logical Languages and Their Complexity
    Christoph Koch (University of Saarlandes)
    The slides (in powerpoint).
    Computing Monadic Fixed-Points in Linear-Time on Doubly-Linked Data Structures
    Steven Lindell (Haverford College)
    The full paper (in PDF).

    27. CS 3240 Languages And Computation
    Undecidable problems, Turing Machines, Halting Problem; Complexity of computation, classes of languages P/NP, space and time completeness
    CS 3240 Languages and Computation
    Spring 2006
    Instructor: Santosh Pande (
    Office: CCB 222
    Phone: 385-2169
    Office Hrs: Tue-Thurs, 12:30 to 1:30 pm (subject to change) TA: Lakshmi Narasimhan Chakrapani
    Email: nsimhan at
    TA Office: CCB Commons Area
    TA Office Hrs: Tue-Wed, 3:15 to 4:15 pm
    Course Description
    Course Outline Slides and course handouts Homework and Project Information
    Course Description back to top
    • In depth understanding of practical applications of regular and context free languages in light of compilers. Understanding of theoretical underpinnings that form the basis of regular and context free languages along with notions of Turing computability. Problem solving combining the two in light of practical pattern matching languages such as Perl.
    This course has both theoretical as well as practical work involved and regular reading and class participation is necessary. Course Outline back to top Textbooks : Bundle #1418879746
    • "Compiler Construction: Principles and Practice", by Kenneth C. Louden,Thompson Course Technology, ISBN 0-534-93972-4 "Introduction to the Theory of Computation" by Michael Sipser, Thompson Course Technology, ISBN 0-534-94728-X

    28. Schloss Dagstuhl : Seminar Homepage
    The seminar Real computation and Complexity was intended as a meeting place of several tendencies in the Complexity analysis of algorithms in real

    29. Martyn Amos
    By arguing for a notion of quality of computation , Westwell reminded me of books and news stories in the areas of biological computing, Complexity,
    @import url(""); @import url(""); var BL_backlinkURL = "";var BL_blogId = "10721553";
    martyn amos
    Friday, December 21, 2007
    Back up and running
    I've decided the give the blog another go, after a few people got in touch to encourage me to continue (well, two people did...) So, I'll resume posting in the New Year; I can't guarantee the frequency, but posts will hopefully offer a little more depth as a result.
    Merry Christmas one and all, and I'll see you in 2008! posted by Martyn Amos @ 5:01 PM 0 comments links to this post
    Friday, August 31, 2007
    My Edinburgh talk
    I had a wonderful time at the Edinburgh Book Festival over the weekend; a full venue and books to sign afterwards makes for a happy author! Here is a lightly edited version of what I had to say.
    Indeed, it wasn't until 1994 that someone demonstrated, for the first time, the feasibility of building computers from molecular-scale bits. Feynman's vision had waited, not only for the technology to catch up, but for a person with the required breadth of understanding and the will to try something slightly bizarre. That person was Len Adleman, who won the computer science equivalent of the Nobel Prize for his role in the development of the encryption scheme that protects our financial details whenever we buy something on the Internet. Len has always had an interest in biology; when one of his students showed him a program that could take over other programs and force them to replicate it, Len said “Hmmm.... that looks very much like how a virus behaves.” The student was Fred Cohen, author of the first ever computer virus, and Len's term stuck. (

    30. ScienceDirect - Knowledge-Based Systems : One Cannot Not Interact
    As long as the Complexity of computation was very low (due to the fact that computers are in our day still in a very early stage of development) and the
    Athens/Institution Login Not Registered? User Name: Password: Remember me on this computer Forgotten password? Home Browse My Settings ... Help Quick Search Title, abstract, keywords Author e.g. j s smith Journal/book title Volume Issue Page Knowledge-Based Systems
    Volume 14, Issue 8
    , 1 December 2001, Pages 437-440
    Full Text + Links PDF (60 K) Related Articles in ScienceDirect Shifting viewpoints: Artificial intelligence and human
    Artificial Intelligence

    Artificial Intelligence Volume 170, Issue 18 December 2006 Pages 1256-1258

    Terry Winograd
    The AI and HCI communities have often been characterized as having opposing views of how humans and computers should interact. As both of them evolve, there is a deeper contrast that cuts across these communities, in how researchers conceive the relationship between knowledge and design. By examining the rationalistic and design orientations underlying bodies of work in both disciplines, we highlight relevant differences and possibilities for effective interaction with computers.
    Abstract + References PDF (81 K) Introduction: Toward a Multidisciplinary Science of Hum...

    31. Summer School And Workshop On Proof Theory, Computation And Complexity
    Like for last year’s events on `Proof Theory and computation´ (Dresden) and `Proof, computation, Complexity´ (Tübingen), we aim at a meeting where people
    Summer School and Workshop on
    Proof Theory, Computation and Complexity
    June 23-July 4, 2003 Call for Participation
    (Dresden) and For attending courses, we ask for a fee of 100 EUR (to be paid in cash at the school). Registration is requested before May 25, 2003; please send an email to PTEvent@Janeway.Inf.TU-Dresden.DE , making sure you include a very brief bio (5-10 lines) stating your experience, interests, home page, etc. We select applicants in case of excessive demand. A limited number of grants covering all expenses is available. Applications for grants must include an estimate for travel costs and they should be sent together with the registration. We provide assistance in finding an accommodation in Dresden. Week 1, June 23-27: courses on
    • Denotational Semantics of Lambda Calculi
      Achim Jung
      (Birmingham, UK)
    • Proof Theory with Deep Inference
      Alessio Guglielmi
      (Dresden, Germany)
    • Semantics and Cut-elimination for Church's (Intuitionistic) Theory of Types, with Applications to Higher-order Logic Programming
      Jim Lipton
      (Wesleyan, USA)

    32. Proof, Computation, Complexity
    The aim of PCC is to stimulate research in proof theory, computation, and Complexity, focusing on issues which combine logical and computational aspects.
    PCC - Proof, Computation, Complexity
    4th International Workshop
    July 16-17, 2005, Lisbon Aims and Scope Contributions Timetable Further information ... (via ICALP'05)
    Aims and Scope
    The aim of PCC is to stimulate research in proof theory, computation, and complexity, focusing on issues which combine logical and computational aspects. Topics may include applications of formal inference systems in computer science, as well as new developments in proof theory motivated by computer science demands. Specific areas of interest are (non-exhaustively listed) foundations for specification and programming languages, logical methods in specification and program development, new developments in structural proof theory, and implicit computational complexity. Past events were held
    PCC is intended to be a lively forum for presenting and discussing recent work. The talks have a duration of

    33. Pure Mathematics In MSCS
    computational Complexity, randomized computation, combinatorics. Gyorgy Turan, Ph.D. Joszef A. University (Hungary), 1981. Complexity theory; computational
    M athematics, S tatistics, and ... cience @ UIC graduate studies undergraduate studies math education MSCS ... faculty research Pure Mathematics SKIP PAST THE INDEX

    34. Atlas Conferences
    DIMACS Workshop Intrinsic Complexity of computation. in Special Year on computational Intractability. April 1013, 2000. Piscataway, NJ, USA. Mathematics
    Atlas home Conferences Abstracts about Atlas
    DIMACS Workshop: Intrinsic Complexity of Computation
    in Special Year on Computational Intractability
    April 10-13, 2000
    Piscataway, NJ, USA
    Host: DIMACS Center, Rutgers University Date received: October 07, 1999 Atlas Conferences Inc.

    35. Geocal06: Geometry Of Computation 2006
    This workshop will be open to contributions on various aspects of Implicit Computational Complexity including (but not exclusively) logical systems,
    Geometry of Computation 2006 (Geocal06)
    Marseille - Luminy
    Monday January 30 - Friday March 3
    • Programme The Geocal06 session consists in an intensive five week series of lectures and workshops in logic, models and semantics of programming languages, theory of concurrency... organized by the GEOCAL group at the CIRM . One of the main goal of this session is to gather researchers working in different areas of theoretical science and let them interact.
      The session is divided into two parts: the first two weeks are devoted to a winter school, the last three weeks to specialised workshops.
      Winter school lectures
      Week 1 (Jan 30 - Feb 3): Algebra and computation ( timetable
      Universal Algebra and Diagrammatic Reasoning ( description Algebraic Topology, Concurrency and Rewriting ( description
      Week 2 (Feb 6 - Feb 10): Logic and computation ( timetable
      Realisability ( description Complexity and Logic ( description
      Click on the workshop links for the programme
      Week 3 (Feb 13 - Feb 17): Logic and interactions ( timetable
      Implicit Computational Complexity ( description Linguistics and logic ( description Dynamics and structure of biological networks ( description
      Week 4 (Feb 20 - Feb 24): Semantics ( timetable
      Geometry of interaction ( description Semantics and games ( description Higher dimensional rewriting, concurrency and directed homotopy (

    36. Special Issue On Implicit Computational Complexity (ICC)
    systems to provide languages for Complexitybounded computation. It aims at studying the computational Complexity of programs without
    ACM Transactions on Computational Logic (TOCL)
    Special Issue on Implicit Computational Complexity (ICC)
    Following the success of the GEOCAL workshop on ICC, there will
    be a special issue of the ACM Transactions on Computational Logic
    Submissions for this special issue are hereby solicited to participants
    of the workshop, but also to other contributors.
    The workshop was held on February 13-17 2006 in Marseille (France)
    as part of the Geometry of Computation 2006 meeting (series of
    lectures and
    workshops) organized by the GEOCAL project. More information can be found at SCOPE Implicit Computational Complexity (ICC) has emerged from various propositions to use logic and formal methods like types, rewriting systems... to provide languages for complexity-bounded computation. It aims at studying the computational complexity of programs without referring to a particular machine model and explicit bounds on time or memory, but instead by relying on logical or computational

    37. Workshop On Implicit Computational Complexity - February 11-12
    Implicit Computational Complexity Implicit Computational Complexity (ICC) has emerged from various propositions to use logic and formal methods like types,

    38. Complexity Theory
    http//, Bulletin of the European Association for Theoretical Computer Science- Computational Complexity
    h o m e
    Search IS theory Web site Search WWW T h e o r i e s U s e d i n I S R e s e a r c h C o m p l e x i t y T h e o r y Theory Name Complexity Theory Acronym N/A Alternate name(s) Computational Complexity Theory Main dependent construct(s)/factor(s) Time (Time Complexity), Space (Memory), Parallel Processors Main independent construct(s)/factor(s) Size of the Input Concise description of theory Complexity theory is part of the theory of computation dealing with the resources required during computation to solve a given problem. The most common resources are time (how many steps it takes to solve a problem) and space (how much memory it takes). Other resources can also be considered, such as how many parallel processors are needed to solve a problem in parallel. Complexity theory differs from computability theory, which deals with whether a problem can be solved at all, regardless of the resources required. After the theory explaining which problems can be solved and which cannot be, it was natural to ask about the relative computational difficulty of computable functions. This is the subject matter of computational complexity.
    The time complexity of a problem is the number of steps that it takes to solve an instance of the problem as a function of the size of the input (usually measured in bits), using the most efficient algorithm. The exact number of steps will depend on exactly what machine or language is being used.

    39. Computational Complexity Conference
    Annual conference that deals with computational Complexity broadly defined.
    IEEE Conference on
    Computational Complexity
    The 23rd Conference
    June 22nd to June 26th, 2008
    University of Maryland, College Park
    Call for papers
    The submission deadline (December 6th) has now passed. Acceptance notifications will be sent out by February 8th.
    The 2008 conference will take place at the University of Maryland, College Park, and will run from Sunday evening, June 22nd, to Thursday, June 26th. The call for papers is now available.
    The 2009 conference will be held in Paris on dates yet to be determined.
    Purpose and Scope
    This is an annual conference that deals with computational complexity broadly defined. It is usually held sometime between mid-May and mid-July and somewhere in North America or Europe. A call-for-papers is issued each summer by August 1st. Among the topics considered in the scope of the conference are:
    • Complexity classes Algebraic complexity Proof complexity Interactive and probabilistic proof systems Circuits complexity Kolmogorov complexity Reducibility and completeness Communication complexity Inapproximability Cryptographic complexity Complexity and learning Complexity and logic Quantum computation Average case complexity Pseudorandomness and derandomization Complexity in other concrete computational models
    This conference is sponsored by the IEEE Computer Society Technical Committee on Mathematical Foundations of Computing . The conference is overseen by a conference committee . Each year, a program committee selects papers to be presented at the conference.

    40. Computational Complexity - Algorithm Analysis And Problem Complexity Journals, B
    Tables of contents from vol.7 (1998) on. Full text to subscribers via LINK. science?SGWID=4-40353-70-1

    41. Computational Complexity Theory
    Another project in Complexity research studies the area called Descriptive Complexity. Computational Complexity was originally defined in terms of the
    Computational Complexity Theory
    A fundamental area of theoretical computer science is complexity theory, the analysis of the resources needed to solve computational problems. Researchers in this area define computational models, such as Turing machines, Boolean Circuits, Parallel Random Access Machines, etc., and resource measures such as space, parallel time, amount of hardware, etc. A complexity class is then the set of problems solvable in a particular model under particular resource constraints. Models must be simple enough to allow mathematical analysis yet general enough to be useful in a wide context. Perhaps the hardest thing to prove about a computational model is that it cannot solve a given problem within particular resource constraints. Such ``lower bounds'' are currently obtainable only when the model is very specialized or the constraints are very severe. The research project in Low-Level Complexity conducted by our group attempts to attack such problems by determining the relationships between complexity classes defined in various models by such very severe constraints. Another project in complexity research studies the area called Descriptive Complexity . Computational complexity was originally defined in terms of the natural entities of time and space, and the term complexity was used to denote the time or space used in the computation. Rather than checking whether an input satisfies a property S, a more natural question might be, what is the complexity of expressing the property S? These two issues checking and expressing are closely related. It is startling how closely tied they are when the latter refers to expressing the property in first-order logic of finite and ordered structures.

    42. Mathematics Workshop, Kaikoura 2000
    Computability, Complexity, and Computational Algebra, . although this will be interpreted broadly. As usual, families are invited to come.
    Mathematics Workshop
    Kaikoura 2000
    January 7-15, 2000
    links to information about the workshop and Kaikoura
    List of Participants
    Talks will be held at the Memorial Hall.
    This year the annual NZMRI summer workshop will be based in beautiful Kaikoura in the South Island of New Zealand. This follows previous workshops in Huia (1994), Tolaga Bay (1996, 1997), Napier (1998) and Raglan (1999). The topic for Kaikoura 2000 will be "Computability, Complexity, and Computational Algebra," although this will be interpreted broadly. As usual, families are invited to come. We especially encourage New Zealand graduate and senior students, and ask that most of the talks be directed at a graduate student level. The standard format is that we have lectures in the morning, the afternoons are left free for individual persuits, families, sightseeing, whale watching, mathematical discussions, and the like, and we have lectures in the early evening after dinner. There will also be one day off (traditionally Wednesday). We have a stellar group of speakers, all of whom are world renowned mathematicians and computer scientists, and very fine speakers. We have asked that the speakers give a series of 1-3 lectures for this workshop, the first two being easily accessible to graduate students.

    43. Optimal Models Of Disjunctive Logic Programs: Semantics, Complexity, And Computa
    We next prove that, for any of all, stable, minimal family of models, brave and cautious reasoning problems have the same computational Complexity,
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    44. - Computational Complexity: A Quantitative Perspective : Marius Zimand :
    Computational Complexity A Quantitative Perspective Marius Zimand ISBN 9780444828415 Book.
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    Computational Complexity: A Quantitative Perspective (Hardcover)
    Author: Marius Zimand Product Image
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    In Stock: Usually ships in 1 to 2 business days. Format: Hardcover from What's this? Format: Hardcover ISBN: Publish Date: Publisher: Elsevier Science Pub Co Dimensions (in Inches) 9.75H x 7L x 0.75T Pages: Sku: More about this product Item#: BHMVXC View similar products Product Summary Reviews There has been a common perception that computational complexity is a theory of "bad news" because its most typical results assert that various real-world and innocent-looking tasks are infeasible. In fact, "bad news" is a relative term, and, indeed, in some situations (e.g., in cryptography), we want an adversary to not be able to perform a certain task. However, a "bad news" result does not automatically become useful in such a scenario. For this to happen, its hardness features have to be quantitatively evaluated and shown to manifest extensively.
    The book undertakes a quantitative analysis of some of the major results in complexity that regard either classes of problems or individual concrete problems. The size of some important classes are studied using resource-bounded topological and measure-theoretical tools. In the case of individual problems, the book studies relevant quantitative attributes such as approximation properties or the number of hard inputs at each length.

    45. Computational Complexity: A Conceptual Perspective [Goldreich]
    It is concerned with the study of the intrinsic Complexity of computational tasks. That is, a typical Complexity theoretic study looks at the computational
    Computational Complexity: A Conceptual Perspective
    Oded Goldreich
    To be published in April 2008.
    Currently, being copyedited by the publisher. Publisher: Cambridge University Press
    Below is the book's tentative preface and Organization . Drafts (and additional related texts) are available from HERE Last updated: Nov. 2007.
    Preface (tentative, Jan. 2007)
    The strive for efficiency is ancient and universal, as time and other resources are always in shortage. Thus, the question of which tasks can be performed efficiently is central to the human experience. A key step towards the systematic study of the aforementioned question is a rigorous definition of the notion of a task and of procedures for solving tasks. These definitions were provided by computability theory, which emerged in the 1930's. This theory focuses on computational tasks, and considers automated procedures (i.e., computing devices and algorithms) that may solve such tasks. In focusing attention on computational tasks and algorithms, computability theory has set the stage for the study of the computational resources (like time) that are required by such algorithms. When this study focuses on the resources that are necessary for any algorithm that solves a particular task (or a task of a particular type), the study becomes part of the theory of Computational Complexity (also known as Complexity Theory). Complexity Theory is a central field of the theoretical foundations of Computer Science. It is concerned with the study of the

    46. Computational Complexity Of Games And Puzzles
    To me, the best puzzles are NPcomplete (although some good puzzles are in P, relying on gaps in human intuition rather than on computational Complexity for
    Computational Complexity of Games and Puzzles
    Many of the games and puzzles people play are interesting because of their difficulty: it requires cleverness to solve them. Often this difficulty can be shown mathematically, in the form of computational intractibility results: every NP-complete problem is in some sense a puzzle, and conversely many puzzles are NP-complete. Two-player games often have higher complexities such as being PSPACE-complete. WW (pp. 218-219; see references I primarily list here real games and puzzles, games that were invented to be played, rather than to be analyzed. So I'm not including some of the more artificial entries in e.g. WW or GJ , such as "sequential truth assignment". If someone can point me to a tournament for these games, a copy of the game sold in stores, or a program for people to play them against their computers, I'll consider adding them. One caveat: NP-completeness is not a concept that applies to a single puzzle or game position, or even a finite collection of positions. It only makes sense to talk about an infinite family of problems as being NP-complete. For this reason games like chess cannot themselves be NP-complete, as they only have a finite (albeit unthinkably large) number of possible positions. In many cases however there is a natural generalization from some finite game or puzzle to an infinite family of game positions on arbitrarily large game boards, in which it makes sense to talk about NP-completeness. The fact that these infinite generalizations are computationally hard gives us some justification for believing that the original finite games are also hard in some less well-defined sense.

    47. CMU Algorithms And Computational Complexity Page
    Algorithms and Computational Complexity group. Members, research projects, courses.
    Algorithms and Computational Complexity at CMU
    CMU has a strong and diverse group in Algorithms and Complexity Theory. The goals of the group are, broadly speaking, to provide a mathematical understanding of fundamental issues in Computer Science, and to use this understanding to produce better algorithms, protocols, and systems. Research interests include data structures, algorithm design, complexity theory, parallel algorithms and languages, machine learning theory, cryptography and security, on-line algorithms and scientific computing. Our Algorithms and Complexity group maintains strong ties to other areas, such as computer systems, programming languages, and artificial intelligence, and we welcome students who have a combination of theoretical and application-oriented research interests. See also the ACO Program Home Page and the ALAddIN home page
    Faculty not shown: L.Blum, Harchol-Balter, Lafferty, Maggs, Miller, O'Donnell, Rudich, Sleator.
    Guy Blelloch Parallel algorithms and languages. Avrim Blum Machine learning, approximation and on-line algorithms, AI planning.

    48. BEATCS Computational Complexity Column
    Complexity Links. IEEE Conference on Computational Complexity Electronic Colloquium on Computational Complexity Scott Aaronson s Complexity Zoo
    Bulletin of the
    European Association for Theoretical Computer Science

    Computational Complexity Column
    Previous Editors:
    Lance Fortnow
    , June 2000 - February 2004
    Eric Allender
    , June 1997 - February 2000
    Juris Hartmanis
    , February 1987 - February 1996 Lane Hemaspaandra edits a sister column for SIGACT News Please contact the editor if you have any comments on the column or suggestions for future column topics.
    Complexity Links
    IEEE Conference on Computational Complexity
    Electronic Colloquium on Computational Complexity
    Scott Aaronson's Complexity Zoo
    Lance Fortnow's Web Log
    Number 91, February, 2007, Polynomial Size Log Depth Circuits: Between NC1 and AC1
    by Meena Mahajan [PDF] [Postscript]
    Number 90, October, 2006, Iterative Decoding of Low-Density Parity Check Codes
    by Venkatesan Guruswami [PDF] [Postscript]
    Number 89, June, 2006, Learning Boolean Functions under the uniform distribution via the Fourier Transform
    by and Wolfgang Lindner [PDF] [Postscript]
    Number 88, February, 2006, Bridges between Algebraic Automata Theory and Complexity Theory
    by Pascal Tesson and [PDF] [Postscript]
    Number 87, October, 2005, Lower Bounds on Quantum Query Complexity

    49. Wiley::Theory Of Computational Complexity
    Theory of Computational Complexity offers a thorough presentation of the fundamentals of Complexity theory, including NPcompleteness theory,
    United States Change Location

    50. Computational Complexity And The Anthropic Principle
    I ll start with a crash course in computational Complexity theory in the basic concepts that we ll need even to talk about these issues.
    Computational Complexity and the Anthropic Principle Scott Aaronson Notes for a talk given at the Stanford Institute for Theoretical Physics, December 15, 2006 When Lenny [Susskind] and Alessandro [Tomasiello] invited me to speak here, I said, "I'll be delighted to, as long you realize that my understanding of string theory is about at the level of Brian Greene's TV show." There's a relevant joke I don't know if you've heard it. A mathematician and an engineer are sitting in on a string theory lecture. The engineer is struggling, while the mathematician is swimming along with no problem. Finally the engineer asks, "How do you do it? How do you visualize these 11-dimensional spaces?" The mathematician says, "It's easy: first I visualize an n-dimensional space, then I set n equal to 11." My background is in theoretical computer science. In this field we don't know anything about 11-dimensional spaces, but we do know something about n-dimensional spaces. So when string theorists talk (as you do now) about a landscape of exponentially many vacua, in some sense you've blundered onto our territory. I guess the first thing I should say is, welcome! What I want to do today is step back from the details of the string-theory Landscape, and tell you what's known about the

    51. Complexity Theory -- From Wolfram MathWorld
    Du, D.Z. and Ko, K.-I. Theory of Computational Complexity. New York; Wiley, 2000. Weisstein, E. W. Books about Computational Complexity.
    Search Site Algebra
    Applied Mathematics

    Calculus and Analysis
    ... Complexity of Algorithms
    Complexity Theory The theory of classifying problems based on how difficult they are to solve. A problem is assigned to the P-problem (polynomial-time) class if the number of steps needed to solve it is bounded by some power of the problem's size. A problem is assigned to the NP-problem (nondeterministic polynomial-time) class if it permits a nondeterministic solution and the number of steps to verify the solution is bounded by some power of the problem's size. The class of P-problems is a subset of the class of NP-problems , but there also exist problems which are not NP There exists no known P algorithm for graph isomorphism testing, although the problem has also not been shown to be NP-complete . In fact, the problem of identifying isomorphic graphs seems to fall in a crack between P and NP-complete, if such a crack exists (Skiena 1990, p. 181), and as a result, the problem is sometimes assigned to a special graph isomorphism complete complexity class. If a solution is known to an NP-problem , it can be reduced to a single polynomial-time verification. A problem is

    52. Complexity Theory: A Modern Approach / Sanjeev Arora And Boaz Barak
    This is a draft of a textbook on computational Complexity theory. It is intended as a text for an advanced undergraduate course or introductory graduate
    Complexity Theory: A Modern Approach
    Sanjeev Arora and Boaz Barak
    Princeton University
    This is a draft of a textbook on computational complexity theory. It is intended as a text for an advanced undergraduate course or introductory graduate course, or as a reference for researchers and students in computer science and allied fields such as mathematics and physics. We plan to keep this rough draft on the web, even after the book is published, as a resource for teachers and students from developing countries. Update: We have pushed back the publication date, and now plan to submit the book by May 2007 , the expected publication date is January 2008. We would be grateful for comments, especially of the following kind:
    • Places in the book that are difficult for non-specialists, e.g., due to inadequate explanations or use of jargon. Incorrect proofs or calculations. Topics you normally teach but are missing (please check the table of contents for topics we still plan to add). Places where, in your experience, students get confused and need additional explanations or a worked-out exercise. Missing or incorrect references.

    53. Computational Complexity: Quantum Computing And Quantum Phy.
    It depends on what quantum computing means. If you mean quantum Complexity theory, then the only quantum mechanics you need is a 30minute introduction to
    @import url(""); @import url(""); var BL_backlinkURL = "";var BL_blogId = "3722233";
    Computational Complexity
    About Computational complexity and other fun stuff in math and computer science as viewed by Bill Gasarch. Blog created and written until March 2007 by Lance Fortnow. My Links Bill's Home Page Lance's Home Page Weblog Home Weblog Archives and Search ... Favorite Theorems Recent Posts Social Process and Proofs of Theorems and Programs... The Koblitz Controversy: A reaction Theory Starts Here! (Informatics Olympiad) RANT about Electronic Refereeing ... Math in Turkey Complexity Links IEEE Conference on Computational Complexity Electronic Colloquium on Computational Complexity BEATCS Computational Complexity Column Complexity Zoo ... Favorite Complexity Books Weblogs Andy Drucker Ars Mathematica Computing Research Policy D. Sivakumar ... Terence Tao Other Links DMANET FYI Nielsen's Principles of Research Parberry's TCS Guides ... Theorynet Discussion Groups Computer Science Theory Theory Edge
    This work is licensed under a Creative Commons License
    Friday, September 07, 2007

    54. Nonlinear Dynamics And Complex Systems Theory (Glossary)
    The Computational Complexity of a problem is then defined as the time it takes for the fastest program running on a universal computer (as measured in
    Nonlinear Dynamics and Complex Systems Theory
    Glossary of Terms
    A B C D ... Z
    Algorithmic Complexity Animats Artificial Life ... Autopoiesis B Backpropagation Algorithm Basin of Attraction Bifurcation Boolean Function C Cantor Set Catastrophe Theory Cellular Automata Cellular Games ... Coupled-Map Lattices D Dissipative Structure Dissipative Dynamical Systems E Edge of Chaos Emergence Entropy Ergodic System ... Evolutionary Stable Strategy F Finite Automata Fitness Landscape Flicker (or 1/f) Noise Fractals ... Fuzzy Logic G Game Theory Genetic Algorithms Genetic Programming Genotype H Hausdorff Dimension Hierarchy Holarchy Homoclinic Point ... Hypercycle I Inductive Learning Information Dimension Information Theory Intermittency ... Iterated Function Systems J K Knowledge Representation L Lattice Gas Models Life Game Limit Cycle Lindenmeyer Systems ... Lyapunov Exponent M Markov Process Maximum Entropy Mean-Field Theory Metastability ... Multifractal N Navier-Stokes Equations Neural Networks Nonlinearity NP-Hard Problems ... NP-Complete O Order Parameter P Pattern Recognition Percolation Theory Petri Nets Phase Space ... Punctuated Equilibrium Q Quasiperiodic R Random Boolean Network Reaction Diffusion Models Relativistic Information Theory S Scaling Laws Search Space Self Organization Self Organized Criticality ... Synergetics T Topological Dimension U Ultrametricity Universality Universal Computer Universal Turing Machine ... Unstable Equilibrium Adaptation Any change in the structure or function of an entity (say, a biological organism) that allows it to survive and reproduce more effectively in its environment.

    55. The 8th Understanding Complex Systems Symposium Will Be May 12-15
    Keywords computational Complexity, algorithmic Complexity, minimal algorithms, NPcomplete, cell as a system, genomics, proteomics, metabolomics,
    The 8 th Understanding Complex Systems Symposium will be May 12-15, 2008 at the UIUC
    C S O Y M S P T L E E M X S
    Symposium Focus this year: Computational Complexity and Bioinformatics
    Video Summaries Images 1 Images 2 Images 3 by Russ Abbott Slides and Audio Files for each Session Student Competition Results May 16-19, 2005
    Department of Physics
    University of Illinois at Urbana-Champaign The symposium Understanding Complex Systems is designed to bring together researchers from many academic disciplines and industry and stimulate cross-disciplinary research activities to build and advance the Complex Systems Research community. A small group of distinguished invited speakers will introduce key complex systems concepts in the context of their discipline. These invited plenary talks are on a 'Scientific American' level. Three hands-on tutorials are in parallel with technical sessions, covering the most recent research findings. The organizers will provide information about funding opportunities for complex systems research and promote linkages for interdisciplinary proposals.

    D.Z. Du and K.-I Ko, Theory of Computational Complexity, Wiley Interscience, New York, 2000. M. Garey and D. Johnson, Computers and Intractability A Guide
    CPS 240 : Computational Complexity
    Name: Pankaj K. Agarwal
    Office: D207 LSRC Bldg
    CPS 140 or equivalent.
    Grading Policy
    • 4 Homeworks; weight 20%
    • One midterm; weight 20%
    • Final: weight 40%
    Text Book
    • C.H. Papadimitriou, Computational Complexity , Addison Wesley, Reading, MA, 1993.
    Reference Books
  • G. Ausiello, P. Crescenzi, G. Gambosi, V. Kann, A. Marchetti-Spaccamela, and M. Protasi, Complexity and Approximation , Springer-Verlag, Heidelberg, 1999.
  • J. Balcazar, J. Diaz, J. Gabarro, Structural Complexity I , Springer-Verlag, Heidelberg, 1988.
  • J. Balcazar, J. Diaz, J. Gabarro, Structural Complexity II , Springer-Verlag, Heidelberg, 1988.
  • M. Davis, R. Sigal, and E. Weyuker, Computability, complexity, and languages , Academic Press, Boston, 1994.
  • D.-Z. Du and K.-I Ko, Theory of Computational Complexity , Wiley Interscience, New York, 2000.
  • M. Garey and D. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, Freeman and Company, New York, 1979.
  • 57. Computational Complexity And The Scope Of Software Patents
    Despite this, the patent system has continued to disregard computational Complexity, an issue of central concern to computer scientists and of strategic
    UNC School of Law
    Van Hecke-Wettach Hall
    100 Ridge Road, CB #3380
    Chapel Hill, NC 27599-3380
    Phone: (919) 962-4116
    Fax: (919) 962-1277 Up The Microsoft Case Antitrust By Chance: A Unified Theory of Horizontal Merger Doctrine [ Computational Complexity and the Scope of Software Patents ] Making the World Wide Web Safe for Democracy: A Medium-Specific First Amendment Analysis Accurate Calculation of Short-Swing Profits Under Section 16(b) of the Securites Exchange Act of 1934 The KKK and Vietnamese Fishermen
    Computational Complexity and the Scope of Software Patents
    Andrew Chin
    Reprinted from 39 Jurimetrics Journal 17 (1998). Permission is hereby granted to download and/or print one copy for personal use. Republication in any form, including electronic, without written consent of the author is prohibited.
    Recent developments in patent law, most notably the effective nullification of the Supreme Court's 1972 Benson decision excluding mathematical algorithms from patentable subject matter, have attempted to reflect an increasingly sophisticated approach to computer science and technology. Despite this, the patent system has continued to disregard computational complexity, an issue of central concern to computer scientists and of strategic importance to U.S. information technology policy. This Article proposes a development of patent scope doctrine that would introduce the issue of computational complexity into patent infringement analysis, thereby encouraging more efficient algorithm design, enhancing public benefits from complementary improvements in computer hardware, and strengthening the institutional competence of the patent system.

    58. Advanced Computational Complexity Theory
    The course covers fundamental concepts of Computational Complexity Theory. We will start with basic techniques such as Analysis of Boolean functions,
    Advanced Computational Complexity Theory
    [Tuesday , Ornstein Syllabus Lecture Notes Assignments Prerequisite Course: Complexity Theory (undergraduate) Instructor: Muli Safra T.A.: Oded Schwartz (hopefully)
    The course covers fundamental concepts of Computational Complexity Theory. We will start with basic techniques such as Analysis of Boolean functions, Coding Theory, and Testing. We then go on to cover basic concepts in Complexity Theory, and move on to discuss Randomness in Computations, Derandomization and a little Cryptography. We will then move on to PCP and Hardness of Approximation problems.
    Harmonic Analysis of Boolean functions, Coding Theory, Testing and Embedding. Time and Space bounded computations Nondetermistic classes, The class NP and the "P vs NP" question, NP-hardness; The polynomial-time hierarchy Small-space computational classes (L and NL) Randomness in computation (BPP and RL) De-randomization, Pseudo random generators

    59. Notions Of Complexity: Information-theoretic, Computational And Statistical Appr
    Notions of Complexity Informationtheoretic, Computational and Statistical Approaches Workshop. 7 - 9 October 2004, Eindhoven, The Netherlands.
    Notions of Complexity: Information-theoretic, Computational and Statistical Approaches Workshop
    7 - 9 October 2004, Eindhoven, The Netherlands.
    Overview The theoretical analysis of systems that learn from data has been an important topic of study in statistics, machine learning, and information theory. In all these paradigms, distinct methods have been developed to deal with inference when the models under consideration can be arbitrarily large. Recently, there has been a fruitful cross-fertilization of ideas and proof techniques. To give but one example, very recently, minimax optimal convergence rates of the information-theoretic MDL method were proved using ideas from the - computational - PAC-Bayesian paradigm and - statistical - empirical process techniques. The goal of this workshop is to bring together leading theoreticians to allow them to debate, compare and cross-fertilise ideas from these distinct inductive principles. At the workshop, we will establish a PASCAL special interest group for `merging computational and information-theoretic learning with statistics'. Invited Speakers (* - to be confirmed)
    E. Belitser

    60. Dieter Van Melkebeek - Research On Computational Complexity Theory
    Some of the most fundamental results in computational Complexity are time hierarchies that we can solve more decision problems on some model of
    Research on Computational Complexity Theory
    Dieter van Melkebeek
    Given a problem, can we realistically solve it using computers? Complexity theory aims to answer this question by describing how many resources we need to compute the solution as a function of the problem size. Typical resources include time on sequential and parallel architectures, and memory space. As we want to abstract away from details of input representation and specifics of the computer model, we end up with classes of problems which we can solve within certain robust resource bounds. We often focus on decision problems, or equivalently, on recognizing the corresponding language of yes instances. Some of the most well-studied complexity classes are:
    • NC : languages decidable in parallel logarithmic time.
    • L: languages decidable in logarithmic space.
    • NL: languages with logarithmic space membership proof systems.
    • P: languages decidable in polynomial time.
    • NP: languages with polynomial time membership proof systems.
    • PSPACE: languages decidable in polynomial space.

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