massivedatasets.wordpress.com
Archive 2013 | Algorithms for Massive Data Sets
https://massivedatasets.wordpress.com/archive-2013
Algorithms for Massive Data Sets. State-of-the-art algorithmic techniques and models for massive data sets. Office hours Friday 12-13, Building 322, room 018. Monday 8.15- 10. Bldg. 308/Aud. 11. Monday 10-12. Bldg. 324/Room 040. The course runs in the DTU spring semester (Feb 4th to May 13th). There is no teaching in the Easter break (March 25th and April 1st). Use the template.tex. Collaboration policy for mandatory exercises. You may collaborate with fellow students on the hand in exercises. J Carter a...
massivedatasets.wordpress.com
Archive 2011 | Algorithms for Massive Data Sets
https://massivedatasets.wordpress.com/archive-2011
Algorithms for Massive Data Sets. State-of-the-art algorithmic techniques and models for massive data sets. The course cover 3 major topics:. Time complexity analysis of randomized search heuristics. Monday 8.15- 10. Building 308, auditorium 11. Group exercises in building 322, room 033. The weekplan is preliminary. Part I: Advanced Data Structures. Week 1: Introduction and Hashing: Chained, Universal, and Perfect. J Carter and M. Wegman, Universal Classes of Hash Functions. J Comp. Sys. Sci. J ACM, 1984.
mathematica-bits.blogspot.com
Mathematica bits: Semidefinite programming in Mathematica using CVXOPT
http://mathematica-bits.blogspot.com/2011/03/semidefinite-programming-in-mathematica.html
Tips for Mathematica users. Thursday, March 3, 2011. Semidefinite programming in Mathematica using CVXOPT. One can get access to semidefinite programming from Mathematica by using Pythonika to interface with Python's cvxopt package. For MacOS 10.6 and Mathematica 8 the following should work. Install 64-bit Python 2.7 distribution from official site. Get latest cvxopt sources. Fill correct paths in Pythonika Makefile, also add "-lstdc -framework CoreFoundation" linker flags. G = GridGraph[{4, 3}];. Gram =...
girlincomputerscience.blogspot.com
Girl in the World of Computer Science: Dealing with NP-Hard Problems: An Introduction to Approximation Algorithms
http://girlincomputerscience.blogspot.com/2014/01/dealing-with-np-hard-problems.html
Girl in the World of Computer Science. Friday, January 31, 2014. Dealing with NP-Hard Problems: An Introduction to Approximation Algorithms. This is just a quick overview on approximation algorithms. It is a broad topic to discuss. For more info rmation. The famous NP-Complete class is known for its possible intractability. NP means non deterministic polynomial. And for a problem to be NP-Complete it has to be. NP (verified in polynomial time) and. NP-Hard (as hard as any other problem in the NP class).
corelab.ntua.gr
Corelab : Μαθήματα : Αλγόριθμοι Δικτύων και Πολυπλοκότητα
http://www.corelab.ntua.gr/courses/netalg
Εργαστήριο Λογικής και Επιστήμης Υπολογισμών. Αλγόριθμοι Δικτύων και Πολυπλοκότητα (ΣΗΜΜΥ, ΣΕΜΦΕ, ΜΠΛΑ). 9679; Παρουσιάσεις εργασιών. Άρης Παγουρτζής, Αν. Καθηγητής . Αγγελική Χαλκή, Υ.Δ. . Το πρώτο μάθημα θα γίνει στις 10/3/2015. Τρίτη 12:00-16:00 Παλιό Κτήριο Ηλεκτρολόγων ΕΜΠ, αίθουσα 1.1.29. Το 2ο μισό κάθε μαθήματος γίνεται σε συνδιδασκαλία με το μάθημα 8ου εξ της ΣΗΜΜΥ "Θεωρία Υπολογισμού". Κατανεμημένα πρωτόκολλα: εκλογή αρχηγού, broadcasting, gossiping, byzantine agreement, secret sharing. Ασύ...
algorithmicthinking.org
Guest Lecturers | PACT
https://algorithmicthinking.org/guest-lecturers
Program in Algorithmic and Combinatorial Thinking. Skip to primary content. Students in the program also get the exciting opportunity to learn from guest lecturers! Read on to see which lecturers have made great presentations for PACT students. Department of Computer Science. Mathematics of Networks and Systems. Department of Computer Science. Computer and Information Science. Fingerprinting and Sublinear Algorithms. Charles C. Fitzmorris Professor. Department of Computer Science. Steiner Tree-Related Pr...
massivedatasets.wordpress.com
Archive 2012 | Algorithms for Massive Data Sets
https://massivedatasets.wordpress.com/archive-2012
Algorithms for Massive Data Sets. State-of-the-art algorithmic techniques and models for massive data sets. Monday 8.15- 10. Bldg 208, Aud. 51. Bldg 210, Rooms 112 and 118. The weekplan is preliminary. It will be updated during the course. Under each week there is a number of suggestions for reading material regarding that weeks lecture. It is not the intention that you read ALL of the papers. It is merely a list of papers and notes where you can read about the subject discussed at the lecture. S Alstrup...
