solving large scale and ill-posed least square problem
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I want to estimate unknown value using least square. my matrix is very large, dense(full numerical) and ill_conditioned. I have a pc with 128-gigabyte memory, In this system, only the calculations of the formation of normal equations can be made and there is not enough memory to perform other calculations. (such as computing singular value decomposition, using Tikhonov regularization or using the recursive least square method,...).
I saw this method Solving very large matrices in “pieces”, can I use such these Ideas for solving an ill-posed least square problem? or is there a "matrix in pieces" method when this matrix is ill-conditioned?
My matrix is a full dense numerical value with the size of 120000*90000.
linear-algebra matrices algorithms least-squares
asked Dec 22 '18 at 20:32
user3779629user3779629
1
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add a comment |
$begingroup$
I want to estimate unknown value using least square. my matrix is very large, dense(full numerical) and ill_conditioned. I have a pc with 128-gigabyte memory, In this system, only the calculations of the formation of normal equations can be made and there is not enough memory to perform other calculations. (such as computing singular value decomposition, using Tikhonov regularization or using the recursive least square method,...).
I saw this method Solving very large matrices in “pieces”, can I use such these Ideas for solving an ill-posed least square problem? or is there a "matrix in pieces" method when this matrix is ill-conditioned?
My matrix is a full dense numerical value with the size of 120000*90000.
linear-algebra matrices algorithms least-squares
asked Dec 22 '18 at 20:32
user3779629user3779629
1
$endgroup$
1
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I would suggest to use randomized linear algebra, see research.fb.com/fast-randomized-svd for instance.
$endgroup$
– Picaud Vincent
Dec 22 '18 at 20:47
add a comment |
$begingroup$
I want to estimate unknown value using least square. my matrix is very large, dense(full numerical) and ill_conditioned. I have a pc with 128-gigabyte memory, In this system, only the calculations of the formation of normal equations can be made and there is not enough memory to perform other calculations. (such as computing singular value decomposition, using Tikhonov regularization or using the recursive least square method,...).
I saw this method Solving very large matrices in “pieces”, can I use such these Ideas for solving an ill-posed least square problem? or is there a "matrix in pieces" method when this matrix is ill-conditioned?
My matrix is a full dense numerical value with the size of 120000*90000.
linear-algebra matrices algorithms least-squares
asked Dec 22 '18 at 20:32
user3779629user3779629
1
$endgroup$
I want to estimate unknown value using least square. my matrix is very large, dense(full numerical) and ill_conditioned. I have a pc with 128-gigabyte memory, In this system, only the calculations of the formation of normal equations can be made and there is not enough memory to perform other calculations. (such as computing singular value decomposition, using Tikhonov regularization or using the recursive least square method,...).
I saw this method Solving very large matrices in “pieces”, can I use such these Ideas for solving an ill-posed least square problem? or is there a "matrix in pieces" method when this matrix is ill-conditioned?
My matrix is a full dense numerical value with the size of 120000*90000.
linear-algebra matrices algorithms least-squares
linear-algebra matrices algorithms least-squares
asked Dec 22 '18 at 20:32
user3779629user3779629
1
asked Dec 22 '18 at 20:32
user3779629user3779629
1
edited Dec 22 '18 at 20:42
user3779629
asked Dec 22 '18 at 20:32
user3779629user3779629
1
asked Dec 22 '18 at 20:32
user3779629user3779629
1
asked Dec 22 '18 at 20:32
user3779629user3779629
1
1
1
$begingroup$
I would suggest to use randomized linear algebra, see research.fb.com/fast-randomized-svd for instance.
$endgroup$
– Picaud Vincent
Dec 22 '18 at 20:47
add a comment |
1
$begingroup$
I would suggest to use randomized linear algebra, see research.fb.com/fast-randomized-svd for instance.
$endgroup$
– Picaud Vincent
Dec 22 '18 at 20:47
1
1
$begingroup$
I would suggest to use randomized linear algebra, see research.fb.com/fast-randomized-svd for instance.
$endgroup$
– Picaud Vincent
Dec 22 '18 at 20:47
$begingroup$
I would suggest to use randomized linear algebra, see research.fb.com/fast-randomized-svd for instance.
$endgroup$
– Picaud Vincent
Dec 22 '18 at 20:47
add a comment |
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I would suggest to use randomized linear algebra, see research.fb.com/fast-randomized-svd for instance.
$endgroup$
– Picaud Vincent
Dec 22 '18 at 20:47