Python vectorization of matrix-vector operation












3















I have a Matrix A with shape (2,2,N) and a Matrix V with shape (2,N)



I want to vectorize the following:



F = np.zeros(N)

for k in xrange(N):

    F[k] = np.dot( A[:,:,k], V[:,k] ).sum()


Any way this can be done with either tensordot or any other numpy function without explicit looping?






python performance numpy vectorization tensordot







share|improve this question





























    3















    I have a Matrix A with shape (2,2,N) and a Matrix V with shape (2,N)



    I want to vectorize the following:



    F = np.zeros(N)
    
for k in xrange(N):
    
    F[k] = np.dot( A[:,:,k], V[:,k] ).sum()


    Any way this can be done with either tensordot or any other numpy function without explicit looping?






    python performance numpy vectorization tensordot







    share|improve this question



























      3












      3








      3








      I have a Matrix A with shape (2,2,N) and a Matrix V with shape (2,N)



      I want to vectorize the following:



      F = np.zeros(N)
      
for k in xrange(N):
      
    F[k] = np.dot( A[:,:,k], V[:,k] ).sum()


      Any way this can be done with either tensordot or any other numpy function without explicit looping?






      python performance numpy vectorization tensordot







      share|improve this question
















      I have a Matrix A with shape (2,2,N) and a Matrix V with shape (2,N)



      I want to vectorize the following:



      F = np.zeros(N)
      
for k in xrange(N):
      
    F[k] = np.dot( A[:,:,k], V[:,k] ).sum()


      Any way this can be done with either tensordot or any other numpy function without explicit looping?






      python performance numpy vectorization tensordot




      python performance numpy vectorization tensordot






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Nov 22 '18 at 12:22









      jpp

      98.7k2159110




      98.7k2159110










      asked Nov 22 '18 at 12:20









      fmonegagliafmonegaglia

      1,62711726




      1,62711726
























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          2














          With np.einsum -



          F = np.einsum('ijk,jk->k',A,V)


          We can optimize it further with optimize flag (check docs) set as True.






          share|improve this answer























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            1 Answer
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            active

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            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            2














            With np.einsum -



            F = np.einsum('ijk,jk->k',A,V)


            We can optimize it further with optimize flag (check docs) set as True.






            share|improve this answer




























              2














              With np.einsum -



              F = np.einsum('ijk,jk->k',A,V)


              We can optimize it further with optimize flag (check docs) set as True.






              share|improve this answer


























                2












                2








                2







                With np.einsum -



                F = np.einsum('ijk,jk->k',A,V)


                We can optimize it further with optimize flag (check docs) set as True.






                share|improve this answer













                With np.einsum -



                F = np.einsum('ijk,jk->k',A,V)


                We can optimize it further with optimize flag (check docs) set as True.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Nov 22 '18 at 12:27









                DivakarDivakar

                155k1485174




                155k1485174






























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