Marcin Jurkiewicz - Publications - Bridge of Knowledge

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Year 2022
  • On zero-error codes produced by greedy algorithms

    We present two greedy algorithms that determine zero-error codes and lower bounds on the zero-error capacity. These algorithms have many advantages, e.g., they do not store a whole product graph in a computer memory and they use the so-called distributions in all dimensions to get better approximations of the zero-error capacity. We also show an additional application of our algorithms.

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Year 2021
  • Bounds on isolated scattering number
    Publication

    The isolated scattering number is a parameter that measures the vulnerability of networks. This measure is bounded by formulas de- pending on the independence number. We present new bounds on the isolated scattering number that can be calculated in polynomial time.

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  • Bounds on isolated scattering number
    Publication

    - Year 2021

    The isolated scattering number is a parameter that measures the vulnerability of networks. This measure is bounded by formulas de- pending on the independence number. We present new bounds on the isolated scattering number that can be calculated in polynomial time.

    Full text to download in external service

  • Graphs hard-to-process for greedy algorithm MIN
    Publication

    We compare results of selected algorithms that approximate the independence number in terms of the quality of constructed solutions. Furthermore, we establish smallest hard- to-process graphs for the greedy algorithm MIN.

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  • On the Characteristic Graph of a Discrete Symmetric Channel

    We present some characterizations of characteristic graphs of row and/or column symmetric channels. We also give a polynomial-time algorithm that decides whether there exists a discrete symmetric channel whose characteristic graph is equal to a given input graph. In addition, we show several applications of our results.

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Year 2020
  • An Approximation of the Zero Error Capacity by a Greedy Algorithm
    Publication

    - Year 2020

    We present a greedy algorithm that determines a lower bound on the zero error capacity. The algorithm has many new advantages, e.g., it does not store a whole product graph in a computer memory and it uses the so-called distributions in all dimensions to get a better approximation of the zero error capacity. We also show an additional application of our algorithm.

  • An Approximation of the Zero Error Capacity by a Greedy Algorithm.
    Publication

    - Year 2020

    We present a greedy algorithm that determines a lower bound on the zero error capacity. The algorithm has many new advantages, e.g., it does not store a whole product graph in a computer memory and it uses the so-called distributions in all dimensions to get a better approximation of the zero error capacity. We also show an additional application of our algorithm.

    Full text to download in external service

Year 2017
Year 2015
Year 2014
  • A survey on known values and bounds on the Shannon capacity
    Publication

    - Year 2014

    In this survey we present exact values and bounds on the Shannon capacity for different classes of graphs, for example for regular graphs and Kneser graphs. Additionally, we show a relation between Ramsey numbers and Shannon capacity.

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  • On practical application of Shannon theory to character recognition and more
    Publication

    - Year 2014

    Let us consider an optical character recognition system, which in particular can be used for identifying objects that were assigned strings of some length. The system is not perfect, for example, it sometimes recognizes wrongly the characters "Y" and "V". What is the largest set of strings of given length for the system under consideration, which can be mutually correctly recognized, and the corresponding objects correctly identified?...

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