Difference between revisions of "Manuals/calci/TENSORPRODUCT"

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(Created page with "<div style="font-size:30px">'''TENSORPRODUCT (a,b) '''</div><br/> OR <div style="font-size:30px">'''MATRIXTENSORPRODUCT (a,b) '''</div><br/> *<math>a</math> and <math>b</math>...")
 
 
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|28 || 72 || 43.2 || 17.5 || 45 ||27 ||10.5 || 27||16.200000000000003
 
|28 || 72 || 43.2 || 17.5 || 45 ||27 ||10.5 || 27||16.200000000000003
 
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3.TENSORPRODUCT([[1,2,3]],[[4,5,6]]).print()
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[
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[4,5,6,8,10,12,12,15,18 ]
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]
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4. TENSORPRODUCT([[1],[2],[3]],[[4],[5],[6]]).print()
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[
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[4 ],
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[5 ],
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[6 ],
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[8 ],
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[10 ],
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[12 ],
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[12 ],
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[15 ],
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[18 ]
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]
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5. TENSORPRODUCT([[1],[2],[3]],[[4],[5]]).print()
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[
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[4 ],
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[5 ],
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[8 ],
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[10 ],
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[12 ],
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[15 ]
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==Related Videos==
 +
 +
{{#ev:youtube|v=qp_zg_TD0qE|280|center|Tens or Product}}
  
 
==See Also==
 
==See Also==

Latest revision as of 03:06, 22 February 2022

TENSORPRODUCT (a,b)


OR

MATRIXTENSORPRODUCT (a,b)


  • and are any two matrices.

Description

  • This function shows the Tensor product of the matrix.
  • In , and are any two matrices.
  • Here matrices and should be square matrix with same order.
  • Tensor product is denoted by .
  • Tensor product is different from general product.
  • The Tensor product is defined by the product two vector spaces V and W is itself a Vector space.
  • It is denoted by .
  • A DYADIC product is the special case of the tensor product between two vectors of the same dimension.
  • The tensor product of V and W is the vector space generated by the symbols , with and .
  • The tensor product from the direct sum vector space, whose dimension is the sum of the dimensions of the two summands:

  • Now consider any 2x2 matrices:

=

Examples

1. TENSORPRODUCT([[2,3],[1,0]],[[10,2],[6,-18]])

20 4 30 6
12 -36 18 -54
10 2 0 0
6 -18 0 0

2. TENSORPRODUCT([[3,7.3,6],[10,11,-6],[8,5,3]],[[12,4,-5],[6,10,3],[3.5,9,5.4]])

36 12 -15 87.6 29.2 -36.5 72 24 -30
18 30 9 43.8 73 21.9 36 60 18
10.5 27 16.200000000000003 25.55 65.7 39.42 21 54 32.400000000000006
120 40 -50 132 44 -55 -72 -24 30
60 100 30 66 110 33 -36 -60 -18
35 90 54 38.5 99 59.400000000000006 -21 -54 -32.400000000000006
96 32 -40 60 20 -25 36 12 -15
48 80 24 30 50 15 18 30 9
28 72 43.2 17.5 45 27 10.5 27 16.200000000000003

3.TENSORPRODUCT(1,2,3,4,5,6).print()

[ [4,5,6,8,10,12,12,15,18 ] ]

4. TENSORPRODUCT([[1],[2],[3]],[[4],[5],[6]]).print()

[ [4 ],

[5 ],

[6 ],

[8 ],

[10 ],

[12 ],

[12 ],

[15 ],

[18 ] ]

5. TENSORPRODUCT([[1],[2],[3]],[[4],[5]]).print()

[ [4 ],

[5 ],

[8 ],

[10 ],

[12 ],

[15 ] ]

Related Videos

Tens or Product

See Also

References