PPT-Cities and Complexity

Gilberto Câmara Based on the book Cities and Complexity by Mike Batty Reuses online material on Battys website wwwspatialcomplexityinfo Münster 1636 Münster 1926. How to celebrate it, leverage it, and NOT avoid it!. Are YOU sufficiently complex?. Simple = Best. FROM OCCAM’S . RAZOR. COMPLEXITY. Are YOU sufficiently complex?. Simple = Best. Simple but no. Simpler. China Theory Week, Aarhus. August 13, 2012. Today’s Goal:. To present new developments in a line of research dating back to 2002, presenting some unexpected connections between. Kolmogorov. Complexity (the theory of randomness), and. and . Hierarchy. Concept of Complexity. “whole is more than the sum of its parts”. Holism. new properties not found in subsystems. “mechanistic explanations of emergence rejected”. Weaker view of emergence. Lecture 1: . Intro; Turing machines; . Class P and NP . . . Indian Institute of Science. About the course. Computational complexity attempts . to classify computational . problems. Lecture 1: . Intro; Turing machines; . Class P and NP . . . Indian Institute of Science. About the course. Computational complexity attempts . to classify computational . problems. How Understanding Complexity Promotes Simplicity. Why is writing software hard?. ^. great. http://www.infoq.com/presentations/Simple-Made-Easy. Our duty as software architects is to understand and manage complexity. Theophilus Benson (tbenson@cs.wisc.edu). Aditya Akella (akella@cs.wisc.edu). David A Maltz (dmaltz@microsoft.com). Enterprise Networks. Intricate logical and physical topologies. Diverse network devices. Adaptive management in a volatile and complex world. Dr Jean Boulton. Visiting Senior Research Fellow, DSPS, University of Bath. Visiting Fellow, . Cranfield. School of Management. Director, Claremont Management Consultants Ltd. Toniann. . Pitassi. University of Toronto. 2-Party Communication Complexity. [Yao]. 2-party communication: . each party has a dataset. . Goal . is to compute a function f(D. A. ,D. B. ). m. 1. m. 2. Fall . 2011. Sukumar Ghosh. What is an algorithm. . A finite set (or sequence) of . precise instructions . for performing a computation. . . . Example: Maxima finding. . . procedure . max. (. What is the best way to measure the time complexity of an algorithm?. - Best-case run time?. - Worst-case run time?. - Average run time?. Which should we try to optimize?. Best-Case Measures. How can we modify almost any algorithm to have a good best-case running time?. For as long as humans have gathered in cities, those cities have had their shining—or shadowy—counterparts. Imaginary cities, potential cities, future cities, perfect cities. It is as if the city itself, its inescapable gritty reality and elbow-to-elbow nature, demands we call into being some alternative, yearned-for better place.   This book is about those cities. It’s neither a history of grand plans nor a literary exploration of the utopian impulse, but rather something different, hybrid, idiosyncratic. It’s a magpie’s book, full of characters and incidents and ideas drawn from cities real and imagined around the globe and throughout history. Thomas More’s allegorical island shares space with Soviet mega-planning Marco Polo links up with James Joyce’s meticulously imagined Dublin the medieval land of Cockaigne meets the hopeful future of Star Trek. With Darran Anderson as our guide, we find common themes and recurring dreams, tied to the seemingly ineluctable problems of our actual cities, of poverty and exclusion and waste and destruction. And that’s where Imaginary Cities becomes more than a mere—if ecstatically entertaining—intellectual exercise: for, as Anderson says, “If a city can be imagined into being, it can be re-imagined.” Every architect, philosopher, artist, writer, planner, or citizen who dreams up an imaginary city offers lessons for our real ones harnessing those flights of hopeful fancy can help us improve the streets where we live.   Though it shares DNA with books as disparate as Calvino’s Invisible Cities and Jane Jacobs’s Death and Life of Great American Cities, there’s no other book quite like Imaginary Cities. After reading it, you’ll walk the streets of your city—real or imagined—with fresh eyes.   Lijie. Chen. MIT. Today’s Topic. Background. . What is Fine-Grained Complexity?. The Methodology of Fine-Grained Complexity. Frontier: Fine-Grained Hardness for Approximation Problems. The Connection. –. Introduction to Programming II. William Killian. Complexity. Two types. Speed . –. Relate operations to input size. Space . –. Relate # bytes to input size. Big-O notation. Machine-independent means for specifying efficiency (complexity).