Computational Complexity Of Algorithms

Computational Complexity of Algorithms is a theoretical framework within computational complexity theory that characterizes the inherent resource requirements, typically time or space, of algorithmic problem-solving as a function of input size, distinguishing it from other complexity measures by focusing on the classification of problems according to their algorithm-specific efficiency.

4.8K

Publications

248.6K

Citations

12.3K

Authors

2.3K

Institutions

Publications per year

2017–2026

1.6K

Authors

12.3K

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    17 publications · 1.6K citations

    16

    h-index

  2. CS
    Christoph Studer

    Cornell University, United States

    14 publications · 1.1K citations

    13

    h-index

  3. TQ
    Trung Q. Duong

    Queen's University Belfast, United Kingdom

    12 publications · 685 citations

    12

    h-index

  4. HV
    H. Vincent Poor

    Princeton University, United States

    14 publications · 821 citations

    12

    h-index

  5. XZ
    Xiaofei Zhang

    Nanjing University of Aeronautics and Astronautics, China

    13 publications · 554 citations

    12

    h-index

1–5 of 12.3K

Venues

  1. IEEE Transactions on Signal Processing

    169 publications · 9.4K citations

    52

    h-index

  2. IEEE Transactions on Wireless Communications

    92 publications · 6.4K citations

    40

    h-index

  3. IEEE Transactions on Communications

    84 publications · 5.5K citations

    39

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  4. IEEE Communications Letters

    102 publications · 3.8K citations

    37

    h-index

  5. IEEE Transactions on Vehicular Technology

    111 publications · 4K citations

    35

    h-index

1–5