Some Notes on the Binary GV Bound for Linear Codes Sixth International Workshop on Optimal Codes and Related Topics June 16 - 22, 2009, Varna, BULGARIA.

Slides:



Advertisements
Similar presentations
Solving connectivity problems parameterized by treewidth in single exponential time Marek Cygan, Marcin Pilipczuk, Michal Pilipczuk Jesper Nederlof, Dagstuhl.
Advertisements

Ordinary Least-Squares
THE WELL ORDERING PROPERTY Definition: Let B be a set of integers. An integer m is called a least element of B if m is an element of B, and for every x.
General Linear Model With correlated error terms  =  2 V ≠  2 I.
Size-estimation framework with applications to transitive closure and reachability Presented by Maxim Kalaev Edith Cohen AT&T Bell Labs 1996.
Properties of SPT schedules Eric Angel, Evripidis Bampis, Fanny Pascual LaMI, university of Evry, France MISTA 2005.
Improved Approximation Algorithms for the Spanning Star Forest Problem Prasad Raghavendra Ning ChenC. Thach Nguyen Atri Rudra Gyanit Singh University of.
Analysis of Algorithms
Polynomial Time Approximation Schemes Presented By: Leonid Barenboim Roee Weisbert.
Linear Obfuscation to Combat Symbolic Execution Zhi Wang 1, Jiang Ming 2, Chunfu Jia 1 and Debin Gao 3 1 Nankai University 2 Pennsylvania State University.
1 Potential for Parallel Computation Module 2. 2 Potential for Parallelism Much trivially parallel computing  Independent data, accounts  Nothing to.
Sketching for M-Estimators: A Unified Approach to Robust Regression
A Randomized Linear-Time Algorithm to Find Minimum Spanning Trees David R. Karger David R. Karger Philip N. Klein Philip N. Klein Robert E. Tarjan.
Designing Floating Codes for Expected Performance Hilary Finucane Zhenming Liu Michael Mitzenmacher.
June 3, 2015Windows Scheduling Problems for Broadcast System 1 Amotz Bar-Noy, and Richard E. Ladner Presented by Qiaosheng Shi.
CMOS Circuit Design for Minimum Dynamic Power and Highest Speed Tezaswi Raja, Dept. of ECE, Rutgers University Vishwani D. Agrawal, Dept. of ECE, Auburn.
1 Distortion-Rate for Non-Distributed and Distributed Estimation with WSNs Presenter: Ioannis D. Schizas May 5, 2005 EE8510 Project May 5, 2005 EE8510.
Causality challenge workshop (IEEE WCCI) June 2, Slide 1 Bernoulli Mixture Models for Markov Blanket Filtering and Classification Mehreen Saeed Department.
A Finite Sample Upper Bound on the Generalization Error for Q-Learning S.A. Murphy Univ. of Michigan CALD: February, 2005.
What is the best way to start? 1.Plug in n = 1. 2.Factor 6n 2 + 5n Let n be an integer. 4.Let n be an odd integer. 5.Let 6n 2 + 5n + 4 be an odd.
Lattices for Distributed Source Coding - Reconstruction of a Linear function of Jointly Gaussian Sources -D. Krithivasan and S. Sandeep Pradhan - University.
Multiuser OFDM with Adaptive Subcarrier, Bit, and Power Allocation Wong, et al, JSAC, Oct
Sketching for M-Estimators: A Unified Approach to Robust Regression Kenneth Clarkson David Woodruff IBM Almaden.
(work appeared in SODA 10’) Yuk Hei Chan (Tom)
Quantum Counters Smita Krishnaswamy Igor L. Markov John P. Hayes.
Ch. 8 & 9 – Linear Sorting and Order Statistics What do you trade for speed?
Polyhedral Optimization Lecture 3 – Part 2
MA/CSSE 473 Day 03 Asymptotics A Closer Look at Arithmetic With another student, try to write a precise, formal definition of “t(n) is in O(g(n))”
Fixed Parameter Complexity Algorithms and Networks.
An Algorithm for the Coalitional Manipulation Problem under Maximin Michael Zuckerman, Omer Lev and Jeffrey S. Rosenschein (Simulations by Amitai Levy)
We will use Gauss-Jordan elimination to determine the solution set of this linear system.
Complexity of algorithms Algorithms can be classified by the amount of time they need to complete compared to their input size. There is a wide variety:
CSC 41/513: Intro to Algorithms Linear-Time Sorting Algorithms.
Asymptotic Enumeration of Binary Matrices with Bounded Row and Column Weights Farzad Parvaresh HP Labs, Palo Alto Joint work with Erik Ordentlich and Ron.
Uncorrectable Errors of Weight Half the Minimum Distance for Binary Linear Codes Kenji Yasunaga * Toru Fujiwara + * Kwansei Gakuin University, Japan +
The Selection Problem. 2 Median and Order Statistics In this section, we will study algorithms for finding the i th smallest element in a set of n elements.
Pooling designs for clone library screening in the inhibitor complex model Department of Mathematics and Science National Taiwan Normal University (Lin-Kou)
Estimation of the derivatives of a digital function with a convergent bounded error Laurent Provot, Yan Gerard * 1 DGCI, April, 6 th 2011 * speaker.
1 CPSC 320: Intermediate Algorithm Design and Analysis July 21, 2014.
1 CPSC 320: Intermediate Algorithm Design and Analysis July 9, 2014.
1 Markov Decision Processes Infinite Horizon Problems Alan Fern * * Based in part on slides by Craig Boutilier and Daniel Weld.
Chapter 9: Selection Order Statistics What are an order statistic? min, max median, i th smallest, etc. Selection means finding a particular order statistic.
Zhuo Peng, Chaokun Wang, Lu Han, Jingchao Hao and Yiyuan Ba Proceedings of the Third International Conference on Emerging Databases, Incheon, Korea (August.
Learning With Bayesian Networks Markus Kalisch ETH Zürich.
Sparse Signals Reconstruction Via Adaptive Iterative Greedy Algorithm Ahmed Aziz, Ahmed Salim, Walid Osamy Presenter : 張庭豪 International Journal of Computer.
1 CPSC 320: Intermediate Algorithm Design and Analysis July 16, 2014.
Perfect and Related Codes
Joint Advanced Student School Compressed Suffix Arrays Compression of Suffix Arrays to linear size Fabian Pache.
1 Asymptotically good binary code with efficient encoding & Justesen code Tomer Levinboim Error Correcting Codes Seminar (2008)
Adding and Subtracting Decimals Intro to Algebra.
1 CPSC 320: Intermediate Algorithm Design and Analysis July 30, 2014.
1.7 Linear Independence. in R n is said to be linearly independent if has only the trivial solution. in R n is said to be linearly dependent if there.
is a linear combination of and depends upon and is called a DEPENDENT set.
1Computer Sciences Department. 2 Advanced Design and Analysis Techniques TUTORIAL 7.
A stochastic nonparametric technique for space-time disaggregation of streamflows May 27, Joint Assembly.
Fast and Efficient Static Compaction of Test Sequences Based on Greedy Algorithms Jaan Raik, Artur Jutman, Raimund Ubar Tallinn Technical University, Estonia.
The Message Passing Communication Model David Woodruff IBM Almaden.
Massive Support Vector Regression (via Row and Column Chunking) David R. Musicant and O.L. Mangasarian NIPS 99 Workshop on Learning With Support Vectors.
Chapter 15 Running Time Analysis. Topics Orders of Magnitude and Big-Oh Notation Running Time Analysis of Algorithms –Counting Statements –Evaluating.
Chapter 9: Selection of Order Statistics What are an order statistic? min, max median, i th smallest, etc. Selection means finding a particular order statistic.
Introduction to Algorithms
Error-Correcting Codes:
Distributed Submodular Maximization in Massive Datasets
Lecture 9 Greedy Strategy
Flow Feasibility Problems
Binary Search Counting
The Selection Problem.
Linear Time Sorting.
Lecture 27 CSE 331 Nov 4, 2016.
Maximum Likelihood Estimation (MLE)
Presentation transcript:

Some Notes on the Binary GV Bound for Linear Codes Sixth International Workshop on Optimal Codes and Related Topics June , 2009, Varna, BULGARIA Dejan Spasov, Marjan Gusev

Agenda Intro The greedy algorithm The Varshamov estimate Main result(s) Proof outline Comparison with other results

The Greedy Algorithm Given d and m ; Initialize H For each add x to H, i f the x is NOT linear combination of d- 2 columns of H x x

The Varshamov’s Estimate The greedy code will have parameters AT LEAST as good as the code parameters that satisfy Example: Let m=32 The greedy [ 8752, 8720, 5 ] does exist Varshamov - [ 2954, 2922, 5 ] Can we find a better estimate?

Main Result The code can be extended to a code provided The existence of can be confirmed by the GV bound or recursively until

Some Intuition 1.Every d -1 columns of are linearly independent 2.Let and let 3.This is OK if 4.But the Varshamov’s estimate will count twice 1 1 n n j j i i

Proof Outline - all vectors that are linear combination of d-2 columns from H Find As long as Keep adding vectors - Varshamov bound

Proof Outline Use only odd number of columns

Further Results The code can be extended to a code provided

Comparison: Elia’s result

Comparison: A. Barg et al

Comparison: Jiang & Vardy

For d/n=const For d/n->0

Conclusion The greedy [ 8752, 8720, 5 ] does exist Varshamov - [ 2954, 2922, 5 ] The Improvement - [ 3100, , 5 ] The asymptotical R≥1-H(δ) ? Generalization