Converting a Linear Programming problem for solving using Machine Learning
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I have a typical linear programming optimization problem with a huge data set (millions of records and upwards of 150 GB data file). The size of the data is causing cost issues for reaching an optimal solution.
Is it possible to convert linear programming problems to machine learning type of problems that can be solved with more cost effective means?
Thanks
learning programming
asked Dec 5 '17 at 20:14
user510557user510557
1
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show 1 more comment
$begingroup$
I have a typical linear programming optimization problem with a huge data set (millions of records and upwards of 150 GB data file). The size of the data is causing cost issues for reaching an optimal solution.
Is it possible to convert linear programming problems to machine learning type of problems that can be solved with more cost effective means?
Thanks
learning programming
asked Dec 5 '17 at 20:14
user510557user510557
1
$endgroup$
$begingroup$
In general machine learning methods are more costly than linear regression. Or do you mean constrained linear optimization?
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– mathreadler
Dec 5 '17 at 20:15
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Yes - that is correct - constrained model.
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– user510557
Dec 5 '17 at 20:20
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Okay, then maybe you are right that it could be faster with some machine learning method. But without any more detailed description of the nature of the data it will be quite difficult to propose an approach.
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– mathreadler
Dec 5 '17 at 20:23
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Thanks for your comments mathreadler. The data is a collection of rows and columns where rows represent some disease that can be treated by a combination of medicines (represented by the columns).. The objective is to find lowest combination of medicines that can treat the disease with maximum potential results. The maximum number of medicines that can be selected should not exceed 4. A medicine can be used to treat more than one disease. If a medicine can treat a disease, there is a 1 value that is placed at the intersection of the disease and medicine. Thanks
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– user510557
Dec 6 '17 at 21:13
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Please note that this is not the real description of the data due to sensitivity issues. But close enough to give an idea.
$endgroup$
– user510557
Dec 6 '17 at 21:17
|
show 1 more comment
$begingroup$
I have a typical linear programming optimization problem with a huge data set (millions of records and upwards of 150 GB data file). The size of the data is causing cost issues for reaching an optimal solution.
Is it possible to convert linear programming problems to machine learning type of problems that can be solved with more cost effective means?
Thanks
learning programming
asked Dec 5 '17 at 20:14
user510557user510557
1
$endgroup$
I have a typical linear programming optimization problem with a huge data set (millions of records and upwards of 150 GB data file). The size of the data is causing cost issues for reaching an optimal solution.
Is it possible to convert linear programming problems to machine learning type of problems that can be solved with more cost effective means?
Thanks
learning programming
learning programming
asked Dec 5 '17 at 20:14
user510557user510557
1
asked Dec 5 '17 at 20:14
user510557user510557
1
asked Dec 5 '17 at 20:14
user510557user510557
1
asked Dec 5 '17 at 20:14
user510557user510557
1
asked Dec 5 '17 at 20:14
user510557user510557
1
1
$begingroup$
In general machine learning methods are more costly than linear regression. Or do you mean constrained linear optimization?
$endgroup$
– mathreadler
Dec 5 '17 at 20:15
$begingroup$
Yes - that is correct - constrained model.
$endgroup$
– user510557
Dec 5 '17 at 20:20
$begingroup$
Okay, then maybe you are right that it could be faster with some machine learning method. But without any more detailed description of the nature of the data it will be quite difficult to propose an approach.
$endgroup$
– mathreadler
Dec 5 '17 at 20:23
$begingroup$
Thanks for your comments mathreadler. The data is a collection of rows and columns where rows represent some disease that can be treated by a combination of medicines (represented by the columns).. The objective is to find lowest combination of medicines that can treat the disease with maximum potential results. The maximum number of medicines that can be selected should not exceed 4. A medicine can be used to treat more than one disease. If a medicine can treat a disease, there is a 1 value that is placed at the intersection of the disease and medicine. Thanks
$endgroup$
– user510557
Dec 6 '17 at 21:13
$begingroup$
Please note that this is not the real description of the data due to sensitivity issues. But close enough to give an idea.
$endgroup$
– user510557
Dec 6 '17 at 21:17
|
show 1 more comment
$begingroup$
In general machine learning methods are more costly than linear regression. Or do you mean constrained linear optimization?
$endgroup$
– mathreadler
Dec 5 '17 at 20:15
$begingroup$
Yes - that is correct - constrained model.
$endgroup$
– user510557
Dec 5 '17 at 20:20
$begingroup$
Okay, then maybe you are right that it could be faster with some machine learning method. But without any more detailed description of the nature of the data it will be quite difficult to propose an approach.
$endgroup$
– mathreadler
Dec 5 '17 at 20:23
$begingroup$
Thanks for your comments mathreadler. The data is a collection of rows and columns where rows represent some disease that can be treated by a combination of medicines (represented by the columns).. The objective is to find lowest combination of medicines that can treat the disease with maximum potential results. The maximum number of medicines that can be selected should not exceed 4. A medicine can be used to treat more than one disease. If a medicine can treat a disease, there is a 1 value that is placed at the intersection of the disease and medicine. Thanks
$endgroup$
– user510557
Dec 6 '17 at 21:13
$begingroup$
Please note that this is not the real description of the data due to sensitivity issues. But close enough to give an idea.
$endgroup$
– user510557
Dec 6 '17 at 21:17
$begingroup$
In general machine learning methods are more costly than linear regression. Or do you mean constrained linear optimization?
$endgroup$
– mathreadler
Dec 5 '17 at 20:15
$begingroup$
In general machine learning methods are more costly than linear regression. Or do you mean constrained linear optimization?
$endgroup$
– mathreadler
Dec 5 '17 at 20:15
$begingroup$
Yes - that is correct - constrained model.
$endgroup$
– user510557
Dec 5 '17 at 20:20
$begingroup$
Yes - that is correct - constrained model.
$endgroup$
– user510557
Dec 5 '17 at 20:20
$begingroup$
Okay, then maybe you are right that it could be faster with some machine learning method. But without any more detailed description of the nature of the data it will be quite difficult to propose an approach.
$endgroup$
– mathreadler
Dec 5 '17 at 20:23
$begingroup$
Okay, then maybe you are right that it could be faster with some machine learning method. But without any more detailed description of the nature of the data it will be quite difficult to propose an approach.
$endgroup$
– mathreadler
Dec 5 '17 at 20:23
$begingroup$
Thanks for your comments mathreadler. The data is a collection of rows and columns where rows represent some disease that can be treated by a combination of medicines (represented by the columns).. The objective is to find lowest combination of medicines that can treat the disease with maximum potential results. The maximum number of medicines that can be selected should not exceed 4. A medicine can be used to treat more than one disease. If a medicine can treat a disease, there is a 1 value that is placed at the intersection of the disease and medicine. Thanks
$endgroup$
– user510557
Dec 6 '17 at 21:13
$begingroup$
Thanks for your comments mathreadler. The data is a collection of rows and columns where rows represent some disease that can be treated by a combination of medicines (represented by the columns).. The objective is to find lowest combination of medicines that can treat the disease with maximum potential results. The maximum number of medicines that can be selected should not exceed 4. A medicine can be used to treat more than one disease. If a medicine can treat a disease, there is a 1 value that is placed at the intersection of the disease and medicine. Thanks
$endgroup$
– user510557
Dec 6 '17 at 21:13
$begingroup$
Please note that this is not the real description of the data due to sensitivity issues. But close enough to give an idea.
$endgroup$
– user510557
Dec 6 '17 at 21:17
$begingroup$
Please note that this is not the real description of the data due to sensitivity issues. But close enough to give an idea.
$endgroup$
– user510557
Dec 6 '17 at 21:17
|
show 1 more comment
1 Answer
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Are you using a binary variable to denote whether a medicine is used for a disease or not ? If yes, did you exclude the combinations of medicines-diseases where the medicine does not cure the disease ?
By excluding these combinations, you will be reducing the size of your math program. For example, you have Binary_Input_Data(Medicine, Disease) = 1 if the medicine can treat the disease. Then, your Binary_Variable(Medicine, Disease) must be generated only when Binary_Input_Data(Medicine, Disease) = 1.
answered Dec 10 '18 at 22:53
msx5423msx5423
1
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$begingroup$
Are you using a binary variable to denote whether a medicine is used for a disease or not ? If yes, did you exclude the combinations of medicines-diseases where the medicine does not cure the disease ?
By excluding these combinations, you will be reducing the size of your math program. For example, you have Binary_Input_Data(Medicine, Disease) = 1 if the medicine can treat the disease. Then, your Binary_Variable(Medicine, Disease) must be generated only when Binary_Input_Data(Medicine, Disease) = 1.
answered Dec 10 '18 at 22:53
msx5423msx5423
1
$endgroup$
add a comment |
$begingroup$
Are you using a binary variable to denote whether a medicine is used for a disease or not ? If yes, did you exclude the combinations of medicines-diseases where the medicine does not cure the disease ?
By excluding these combinations, you will be reducing the size of your math program. For example, you have Binary_Input_Data(Medicine, Disease) = 1 if the medicine can treat the disease. Then, your Binary_Variable(Medicine, Disease) must be generated only when Binary_Input_Data(Medicine, Disease) = 1.
answered Dec 10 '18 at 22:53
msx5423msx5423
1
$endgroup$
add a comment |
$begingroup$
Are you using a binary variable to denote whether a medicine is used for a disease or not ? If yes, did you exclude the combinations of medicines-diseases where the medicine does not cure the disease ?
By excluding these combinations, you will be reducing the size of your math program. For example, you have Binary_Input_Data(Medicine, Disease) = 1 if the medicine can treat the disease. Then, your Binary_Variable(Medicine, Disease) must be generated only when Binary_Input_Data(Medicine, Disease) = 1.
answered Dec 10 '18 at 22:53
msx5423msx5423
1
$endgroup$
Are you using a binary variable to denote whether a medicine is used for a disease or not ? If yes, did you exclude the combinations of medicines-diseases where the medicine does not cure the disease ?
By excluding these combinations, you will be reducing the size of your math program. For example, you have Binary_Input_Data(Medicine, Disease) = 1 if the medicine can treat the disease. Then, your Binary_Variable(Medicine, Disease) must be generated only when Binary_Input_Data(Medicine, Disease) = 1.
answered Dec 10 '18 at 22:53
msx5423msx5423
1
answered Dec 10 '18 at 22:53
msx5423msx5423
1
answered Dec 10 '18 at 22:53
msx5423msx5423
1
answered Dec 10 '18 at 22:53
msx5423msx5423
1
1
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$begingroup$
In general machine learning methods are more costly than linear regression. Or do you mean constrained linear optimization?
$endgroup$
– mathreadler
Dec 5 '17 at 20:15
$begingroup$
Yes - that is correct - constrained model.
$endgroup$
– user510557
Dec 5 '17 at 20:20
$begingroup$
Okay, then maybe you are right that it could be faster with some machine learning method. But without any more detailed description of the nature of the data it will be quite difficult to propose an approach.
$endgroup$
– mathreadler
Dec 5 '17 at 20:23
$begingroup$
Thanks for your comments mathreadler. The data is a collection of rows and columns where rows represent some disease that can be treated by a combination of medicines (represented by the columns).. The objective is to find lowest combination of medicines that can treat the disease with maximum potential results. The maximum number of medicines that can be selected should not exceed 4. A medicine can be used to treat more than one disease. If a medicine can treat a disease, there is a 1 value that is placed at the intersection of the disease and medicine. Thanks
$endgroup$
– user510557
Dec 6 '17 at 21:13
$begingroup$
Please note that this is not the real description of the data due to sensitivity issues. But close enough to give an idea.
$endgroup$
– user510557
Dec 6 '17 at 21:17