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002-Contents.Pdf
CubeRoot Contents Contents Contents Purple denotes upcoming contents. 1 Preface 2 Signatures of Top Cubers in the World 3 Quotes 4 Photo Albums 5 Getting Started 5.1 Cube History 5.2 WCA Events 5.3 WCA Notation 5.4 WCA Competition Tutorial 5.5 Tips to Cubers 6 Rubik's Cube 6.1 Beginner 6.1.1 LBL Method (Layer-By-Layer) 6.1.2 Finger and Toe Tricks 6.1.3 Optimizing LBL Method 6.1.4 4LLL Algorithms 6.2 Intermediate 进阶 6.2.1 Triggers 6.2.2 How to Get Faster 6.2.3 Practice Tips 6.2.4 CN (Color Neutrality) 6.2.5 Lookahead 6.2.6 CFOP Algorithms 6.2.7 Solve Critiques 3x3 - 12.20 Ao5 6.2.8 Solve Critiques 3x3 - 13.99 Ao5 6.2.9 Cross Algorithms 6.2.10 Xcross Examples 6.2.11 F2L Algorithms 6.2.12 F2L Techniques 6.2.13 Multi-Angle F2L Algorithms 6.2.14 Non-Standard F2L Algorithms 6.2.15 OLL Algorithms, Finger Tricks and Recognition 6.2.16 PLL Algorithms and Finger Tricks 6.2.17 CP Look Ahead 6.2.18 Two-Sided PLL Recognition 6.2.19 Pre-AUF CubeRoot Contents Contents 7 Speedcubing Advice 7.1 How To Get Faster 7.2 Competition Performance 7.3 Cube Maintenance 8 Speedcubing Thoughts 8.1 Speedcubing Limit 8.2 2018 Plans, Goals and Predictions 8.3 2019 Plans, Goals and Predictions 8.4 Interviewing Feliks Zemdegs on 3.47 3x3 WR Single 9 Advanced - Last Slot and Last Layer 9.1 COLL Algorithms 9.2 CxLL Recognition 9.3 Useful OLLCP Algorithms 9.4 WV Algorithms 9.5 Easy VLS Algorithms 9.6 BLE Algorithms 9.7 Easy CLS Algorithms 9.8 Easy EOLS Algorithms 9.9 VHLS Algorithms 9.10 Easy OLS Algorithms 9.11 ZBLL Algorithms 9.12 ELL Algorithms 9.13 Useful 1LLL Algorithms -
Generating Single and Multiple Cooperative Heuristics for the One Dimensional Bin Packing Problem Using a Single Node Genetic Programming Island Model
Generating Single and Multiple Cooperative Heuristics for the One Dimensional Bin Packing Problem Using a Single Node Genetic Programming Island Model Kevin Sim Emma Hart Institute for Informatics and Digital Innovation Institute for Informatics and Digital Innovation Edinburgh Napier University Edinburgh Napier University Edinburgh, Scotland, UK Edinburgh, Scotland, UK [email protected] [email protected] ABSTRACT exhibit superior performance than similar human designed Novel deterministic heuristics are generated using Single Node heuristics and can be used to produce collections of heuris- Genetic Programming for application to the One Dimen- tics that collectively maximise the potential of selective HH. sional Bin Packing Problem. First a single deterministic The papers contribution is two fold. Single heuristics ca- heuristic was evolved that minimised the total number of pable of outperforming a range of well researched deter- bins used when applied to a set of 685 training instances. ministic heuristics are generated by combining features ex- Following this, a set of heuristics were evolved using a form tracted from those heuristics. Secondly an island model[19] of cooperative co-evolution that collectively minimise the is adapted to use multiple SNGP implementations to gener- number of bins used across the same set of problems. Re- ate diverse sets of heuristics which collectively outperform sults on an unseen test set comprising a further 685 prob- any of the single heuristics when used in isolation. Both ap- lem instances show that the single evolved heuristic out- proaches are trained and evaluated using equal divisions of a performs existing deterministic heuristics described in the large set of 1370 benchmark problem instances sourced from literature. -
13. Mathematics University of Central Oklahoma
Southwestern Oklahoma State University SWOSU Digital Commons Oklahoma Research Day Abstracts 2013 Oklahoma Research Day Jan 10th, 12:00 AM 13. Mathematics University of Central Oklahoma Follow this and additional works at: https://dc.swosu.edu/ordabstracts Part of the Animal Sciences Commons, Biology Commons, Chemistry Commons, Computer Sciences Commons, Environmental Sciences Commons, Mathematics Commons, and the Physics Commons University of Central Oklahoma, "13. Mathematics" (2013). Oklahoma Research Day Abstracts. 12. https://dc.swosu.edu/ordabstracts/2013oklahomaresearchday/mathematicsandscience/12 This Event is brought to you for free and open access by the Oklahoma Research Day at SWOSU Digital Commons. It has been accepted for inclusion in Oklahoma Research Day Abstracts by an authorized administrator of SWOSU Digital Commons. An ADA compliant document is available upon request. For more information, please contact [email protected]. Abstracts from the 2013 Oklahoma Research Day Held at the University of Central Oklahoma 05. Mathematics and Science 13. Mathematics 05.13.01 A simplified proof of the Kantorovich theorem for solving equations using scalar telescopic series Ioannis Argyros, Cameron University The Kantorovich theorem is an important tool in Mathematical Analysis for solving nonlinear equations in abstract spaces by approximating a locally unique solution using the popular Newton-Kantorovich method.Many proofs have been given for this theorem using techniques such as the contraction mapping principle,majorizing sequences, recurrent functions and other techniques.These methods are rather long,complicated and not very easy to understand in general by undergraduate students.In the present paper we present a proof using simple telescopic series studied first in a Calculus II class. -
Kurze Geschichte Des Würfels (Unknown Author)
Kurze Geschichte des Würfels (unknown author) ........................................................................................ 1 Erno Rubik .......................................................................................................................................... 1 Die Herstellung des Original-Rubik-Würfels in Ungarn ................................................................ 3 Die Rubik-Würfel-Weltmeisterschaft ............................................................................................... 6 A Rubik's Cube Chronology (Mark Longridge) .............................................................................................. 8 From five thousand to fifteen millions ....................................................................................................... 11 Toy-BUSINESS KONSUMEX .......................................................................................................................... 14 HISTORY (Nagy Olivér) ................................................................................................................................ 15 Kurze Geschichte des Würfels (unknown author) Jede Erfindung hat ein offizielles Geburtsdatum. Das Geburtsdatum des Würfels ist 1974, das Jahr, in dem der erste funktionsfähige Prototyp entstand und die erste Patentanmeldung entworfen wurde. Der Geburtsort war Budapest, die Hauptstadt Ungarns. Der Name des Erfinders ist inzwischen überall bekannt. Damals war Erno Rubik ein Dozent an der Fakultät für Innenarchitektur an der Akademie -
Mathematics of the Rubik's Cube
Mathematics of the Rubik's cube Associate Professor W. D. Joyner Spring Semester, 1996{7 2 \By and large it is uniformly true that in mathematics that there is a time lapse between a mathematical discovery and the moment it becomes useful; and that this lapse can be anything from 30 to 100 years, in some cases even more; and that the whole system seems to function without any direction, without any reference to usefulness, and without any desire to do things which are useful." John von Neumann COLLECTED WORKS, VI, p. 489 For more mathematical quotes, see the first page of each chapter below, [M], [S] or the www page at http://math.furman.edu/~mwoodard/mquot. html 3 \There are some things which cannot be learned quickly, and time, which is all we have, must be paid heavily for their acquiring. They are the very simplest things, and because it takes a man's life to know them the little new that each man gets from life is very costly and the only heritage he has to leave." Ernest Hemingway (From A. E. Hotchner, PAPA HEMMINGWAY, Random House, NY, 1966) 4 Contents 0 Introduction 13 1 Logic and sets 15 1.1 Logic................................ 15 1.1.1 Expressing an everyday sentence symbolically..... 18 1.2 Sets................................ 19 2 Functions, matrices, relations and counting 23 2.1 Functions............................. 23 2.2 Functions on vectors....................... 28 2.2.1 History........................... 28 2.2.2 3 × 3 matrices....................... 29 2.2.3 Matrix multiplication, inverses.............. 30 2.2.4 Muliplication and inverses............... -
Three-Dimensional Bin-Packing Approaches, Issues, and Solutions
Three Dimensional Bin-Packing Issues and Solutions Seth Sweep University of Minnesota, Morris [email protected] Abstract This paper reviews the three-dimensional version of the classic NP-hard bin-packing, optimization problem along with its theoretical relevance and practical importance. Also, new approximation solutions are introduced based on prior research. The three-dimensional bin-packing optimization problem concerns placing box-shaped objects of arbitrary size and number into a box-shaped, three-dimensional space efficiently. Approximation algorithms become a guide that attempts to place objects in the least amount of space and time. As a NP-hard problem, three-dimensional bin- packing holds much academic interest to computer scientists. However, this problem also has relevance in industrial settings such as shipping cargo and efficiently designing machinery with replaceable parts, such as automobiles. Industry relies on algorithms to provide good solutions to versions of this problem. This research project adapted common approaches to one-dimensional bin-packing, such as next fit and best fit, to three dimensions. Adaptation of these algorithms is possible by dividing the space of a bin. Then, by paying attention strictly to the width of each object, the one-dimensional approaches attempt to efficiently pack into the divided compartments of the original bin. Through focusing entirely on width, these divisions effectively become multiple one-dimensional bins. However, entirely disregarding the volume of the bin by ignoring height and depth may generate inefficient packings. As this paper details in more depth, generally cubical objects may pack well but exceptionally high or long objects leave large gaps of empty space unpacked. -
Breaking an Old Code -And Beating It to Pieces
Breaking an Old Code -And beating it to pieces Daniel Vu - 1 - Table of Contents About the Author................................................ - 4 - Notation ............................................................... - 5 - Time for Some Cube Math........................................................................... Error! Bookmark not defined. Layer By Layer Method................................... - 10 - Step One- Cross .................................................................................................................................. - 10 - Step Two- Solving the White Corners ................................................................................................. - 11 - Step Three- Solving the Middle Layer................................................................................................. - 11 - Step Four- Orient the Yellow Edges.................................................................................................... - 12 - Step Five- Corner Orientation ............................................................................................................ - 12 - Step Six- Corner Permutation ............................................................................................................. - 13 - Step Seven- Edge Permutation............................................................................................................ - 14 - The Petrus Method........................................... - 17 - Step One- Creating the 2x2x2 Block .................................................................................................. -