FAµST python toolbox

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FAµST logo pipeline status coverage python3

The FAµST toolbox provides algorithms and data structures to decompose a given dense matrix into a product of sparse matrices in order to reduce its computational complexity (both for storage and manipulation).

FaµST can be used to:

  • speed up / reduce the memory footprint of iterative algorithms commonly used for solving high dimensional linear inverse problems,
  • learn dictionaries with an intrinsically efficient implementation,
  • compute (approximate) fast Fourier transforms on graphs.

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This version

3.42.2

3.42.2a0 pre-release

3.42.1

3.42.1a3 pre-release

3.42.0

3.42.0a2 pre-release

3.41.0

3.41.0a2 pre-release

3.40.3a2 pre-release

3.40.2

3.40.1

3.40.0

3.40.0a0 pre-release

3.39.25

3.39.25a0 pre-release

3.39.24

3.39.24a0 pre-release

3.39.23

3.39.23a0 pre-release

3.39.22

3.39.22a0 pre-release

3.39.21

3.39.21a0 pre-release

3.39.20

3.39.20a0 pre-release

3.39.19

3.39.19a1 pre-release

3.39.19a0 pre-release

3.39.18

3.39.17

3.39.16

3.39.15

3.39.14

3.39.13

3.39.12

3.39.11

3.39.10

3.39.9

3.39.8

3.39.7

3.39.6

3.39.5

3.39.4

3.39.3

3.39.2

3.39.1

3.39.0

3.38.23

3.38.22

3.38.21

3.38.20

3.38.19

3.38.18

3.38.17

3.38.16

3.38.15

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3.38.11

3.38.10

3.38.9

3.38.8

3.38.7

3.38.6

3.38.4

3.38.3

3.38.2

3.38.1

3.38.0

3.37.13

3.37.12

3.37.11

3.37.10

3.37.9

3.37.8

3.37.7

3.37.6

3.37.5

3.37.4

3.37.3

3.37.2

3.37.1

3.37.0

3.36.0

3.35.18

3.35.17

3.35.16

3.35.15

3.35.14

3.35.13

3.35.11

3.35.10

3.35.9

3.35.8

3.35.7

3.35.6

3.35.5

3.35.4

3.35.3

3.35.0

3.33.5

3.33.4

3.33.3

3.33.2

3.33.1

3.33.0

3.32.5

3.32.4

3.32.3

3.32.2

3.32.1

3.32.0

3.31.6

3.31.5

3.31.4

3.31.3

3.31.2

3.31.1

3.31.0

3.30.0

3.29.1

3.29.0

3.28.12

3.28.10

3.28.9

3.28.8

3.28.7

3.28.6

3.28.5

3.28.4

3.28.3

3.28.2

3.28.1

3.28.0

3.27.2

3.27.1

3.27.0

3.26.7

3.26.6

3.26.5

3.26.4

3.26.3

3.26.2

3.26.1

3.26.0

3.25.10

3.25.9

3.25.8

3.25.7

3.25.6

3.25.5

3.25.4

3.25.3

3.25.2

3.25.1

3.25.0

3.24.2

3.24.1

3.24.0

3.23.2

3.23.1

3.22.0

3.21.0

3.20.0

3.19.0

3.18.1

3.18.0

3.17.0

3.16.0

3.15.0

3.14.1

3.13.0

3.12.0

3.11.1

3.11.0

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