LEADER 00000cai 2200589Ii 4500
001 ocn952520930
003 OCoLC
005 20220607213020.0
006 m o d
007 cr mn|||||||||
008 160629c20129999mauuu o 0 2eng d
020 9781449314620
020 1449314627
035 (OCoLC)952520930
040 NLGGC|beng|cNLGGC|dCOF|dOCLCF|dORU|dOCL|dSOI|dRRP|dINT
|dOCLCQ|dOCLCO
049 STJJ
050 4 QA76.73.P98|b.D69eb
100 1 Downey, Allen.
245 10 Think complexity /|cAllen B. Downey.
260 Needham, Massachusetts :|bGreen Tea Press,|c2012-
264 2 Minneapolis :|bOpen Textbook Library
264 4 |c©2012-
300 1 online resource :|billustrations.
310 Updated irregularly.
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
347 data file|2rda
490 0 Green Tea Press : open access computer science e-books
490 1 Open textbook library
504 Includes bibliographical references and index.
505 0 1. Complexity Science -- 2. Graphs -- 3. Analysis of
algorithms -- 4. Small world graphs -- 5. Scale-free
networks -- 6. Cellular Automata -- 7. Game of Life -- 8.
Fractals -- 9. Self-organized criticality -- 10. Agent-
based models -- 11. Case study: Sugarscape -- 12. Case
study: Ant trails -- 13. Case study: Directed graphs and
knots -- Case study: The Volunteer's Dilemma.
520 "This book is about complexity science, data structures
and algorithms, intermediate programming in Python, and
the philosophy of science: Data structures and algorithms:
A data structure is a collection that contains data
elements organized in a way that supports particular
operations. For example, a dictionary organizes key-value
pairs in a way that provides fast mapping from keys to
values, but mapping from values to keys is generally
slower. An algorithm is a mechanical process for
performing a computation. Designing efficient programs
often involves the co-evolution of data structures and the
algorithms that use them. For example, the first few
chapters are about graphs, a data structure that is a good
implementation of a graph---nested dictionaries---and
several graph algorithms that use this data structure.
Python programming: This book picks up where Think Python
leaves off. I assume that you have read that book or have
equivalent knowledge of Python. As always, I will try to
emphasize fundmental ideas that apply to programming in
many languages, but along the way you will learn some
useful features that are specific to Python. Computational
modeling: A model is a simplified description of a system
that is useful for simulation or analysis. Computational
models are designed to take advantage of cheap, fast
computation. Philosophy of science: The models and results
in this book raise a number of questions relevant to the
philosophy of science, including the nature of scientific
laws, theory choice, realism and instrumentalism, holism
and reductionism, and Bayesian epistemology. This book
focuses on discrete models, which include graphs, cellular
automata, and agent-based models. They are often
characterized by structure, rules and transitions rather
than by equations. They tend to be more abstract than
continuous models; in some cases there is no direct
correspondence between the model and a physical system.
Complexity science is an interdisciplinary field---at the
intersection of mathematics, computer science and physics-
--that focuses on these kinds of models."--Open Textbook
Library.
538 Mode of access: World Wide Web.
588 This bibliographic record is available under the Creative
Commons CC0 "No Rights Reserved" license.
588 0 Online version, 1.2.3; title from PDF (viewed on July 27,
2016).
590 Promoted: Local to Global Cooperative|bOpen Textbook
Library
650 0 Python (Computer program language)
650 0 Computational complexity.
650 7 Computational complexity.|2fast|0(OCoLC)fst00871991
650 7 Python (Computer program language)|2fast
|0(OCoLC)fst01084736
655 7 Textbooks|2fast|0(OCoLC)fst01423863
655 7 Textbooks.|2lcgft
830 0 Open Textbook Library.
914 ocn952520930
994 92|bSTJ
