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CVXPY constraint on 2d array with 3 elements referenced simultaneously
How can foreign key constraints be temporarily disabled using T-SQL?How do I remove an element from a list by index in Python?Getting the last element of a list in PythonHow do I get the number of elements in a list in Python?Peak detection in a 2D arrayDelete an element from a dictionaryHow to define a two-dimensional array in PythonTechniques for Tracing ConstraintsDefining a soft constraint in cvxpyMosek solver failing when constraints added to optimisation problem (10000 variable, using Python/cvxpy)
I have a matrix S of size VxV. I want to impose several contraints on it of the form: 
. I have attempted to do so, but this code runs into the issue ValueError: Atoms must be at most 2D.
I put together a simplified example of my problem:
def ILP_example(scores):
V = scores.shape[0]
u, v, w = np.meshgrid(range(V), range(V), range(V))
arr = cp.Variable(scores.shape)
objective = cp.Maximize(
cp.sum(cp.multiply(scores, arr))
)
constraints = [
arr[u, v] + arr[v, w] - arr[u, w] <= 1,
]
prob = cp.Problem(objective, constraints)
prob.solve()
return
Attempting to run this, for example with ILP_example(np.random.rand(5, 5)) results in the error ValueError: Atoms must be at most 2D. How do I fix this?
python numpy matrix constraints cvxpy
asked Mar 8 at 22:36
Sandeep DcunhaSandeep Dcunha
1199
add a comment |
I have a matrix S of size VxV. I want to impose several contraints on it of the form: . I have attempted to do so, but this code runs into the issue ValueError: Atoms must be at most 2D.
I put together a simplified example of my problem:
def ILP_example(scores):
V = scores.shape[0]
u, v, w = np.meshgrid(range(V), range(V), range(V))
arr = cp.Variable(scores.shape)
objective = cp.Maximize(
cp.sum(cp.multiply(scores, arr))
)
constraints = [
arr[u, v] + arr[v, w] - arr[u, w] <= 1,
]
prob = cp.Problem(objective, constraints)
prob.solve()
return
Attempting to run this, for example with ILP_example(np.random.rand(5, 5)) results in the error ValueError: Atoms must be at most 2D. How do I fix this?
python numpy matrix constraints cvxpy
asked Mar 8 at 22:36
Sandeep DcunhaSandeep Dcunha
1199
add a comment |
I have a matrix S of size VxV. I want to impose several contraints on it of the form: . I have attempted to do so, but this code runs into the issue ValueError: Atoms must be at most 2D.
I put together a simplified example of my problem:
def ILP_example(scores):
V = scores.shape[0]
u, v, w = np.meshgrid(range(V), range(V), range(V))
arr = cp.Variable(scores.shape)
objective = cp.Maximize(
cp.sum(cp.multiply(scores, arr))
)
constraints = [
arr[u, v] + arr[v, w] - arr[u, w] <= 1,
]
prob = cp.Problem(objective, constraints)
prob.solve()
return
Attempting to run this, for example with ILP_example(np.random.rand(5, 5)) results in the error ValueError: Atoms must be at most 2D. How do I fix this?
python numpy matrix constraints cvxpy
asked Mar 8 at 22:36
Sandeep DcunhaSandeep Dcunha
1199
I have a matrix S of size VxV. I want to impose several contraints on it of the form: . I have attempted to do so, but this code runs into the issue ValueError: Atoms must be at most 2D.
I put together a simplified example of my problem:
def ILP_example(scores):
V = scores.shape[0]
u, v, w = np.meshgrid(range(V), range(V), range(V))
arr = cp.Variable(scores.shape)
objective = cp.Maximize(
cp.sum(cp.multiply(scores, arr))
)
constraints = [
arr[u, v] + arr[v, w] - arr[u, w] <= 1,
]
prob = cp.Problem(objective, constraints)
prob.solve()
return
Attempting to run this, for example with ILP_example(np.random.rand(5, 5)) results in the error ValueError: Atoms must be at most 2D. How do I fix this?
python numpy matrix constraints cvxpy
python numpy matrix constraints cvxpy
asked Mar 8 at 22:36
Sandeep DcunhaSandeep Dcunha
1199
asked Mar 8 at 22:36
Sandeep DcunhaSandeep Dcunha
1199
asked Mar 8 at 22:36
Sandeep DcunhaSandeep Dcunha
1199
asked Mar 8 at 22:36
Sandeep DcunhaSandeep Dcunha
1199
asked Mar 8 at 22:36
Sandeep DcunhaSandeep Dcunha
1199
1199
add a comment |
add a comment |
1 Answer
1
active
oldest
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It seems that cvxpy doesn't support more than 2 dimensions, which is what you are doing when you are indexing arr with u, v and w.
As an alternative you can simply reshape those indexing variables so they are 1 dimensional:
u, v, w = [x.reshape(-1) for x in np.meshgrid(range(V), range(V), range(V))]
Then this works just fine:
constraints = [arr[u, v] + arr[v, w] + arr[u, w] <= 1]
arr[u, v] is a 125 length vector now:
Expression(AFFINE, UNKNOWN, (125,))
answered Mar 9 at 21:43
Jacques KvamJacques Kvam
1,29011222
add a comment |
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1 Answer
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1 Answer
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active
oldest
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active
oldest
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active
oldest
votes
It seems that cvxpy doesn't support more than 2 dimensions, which is what you are doing when you are indexing arr with u, v and w.
As an alternative you can simply reshape those indexing variables so they are 1 dimensional:
u, v, w = [x.reshape(-1) for x in np.meshgrid(range(V), range(V), range(V))]
Then this works just fine:
constraints = [arr[u, v] + arr[v, w] + arr[u, w] <= 1]
arr[u, v] is a 125 length vector now:
Expression(AFFINE, UNKNOWN, (125,))
answered Mar 9 at 21:43
Jacques KvamJacques Kvam
1,29011222
add a comment |
It seems that cvxpy doesn't support more than 2 dimensions, which is what you are doing when you are indexing arr with u, v and w.
As an alternative you can simply reshape those indexing variables so they are 1 dimensional:
u, v, w = [x.reshape(-1) for x in np.meshgrid(range(V), range(V), range(V))]
Then this works just fine:
constraints = [arr[u, v] + arr[v, w] + arr[u, w] <= 1]
arr[u, v] is a 125 length vector now:
Expression(AFFINE, UNKNOWN, (125,))
answered Mar 9 at 21:43
Jacques KvamJacques Kvam
1,29011222
add a comment |
It seems that cvxpy doesn't support more than 2 dimensions, which is what you are doing when you are indexing arr with u, v and w.
As an alternative you can simply reshape those indexing variables so they are 1 dimensional:
u, v, w = [x.reshape(-1) for x in np.meshgrid(range(V), range(V), range(V))]
Then this works just fine:
constraints = [arr[u, v] + arr[v, w] + arr[u, w] <= 1]
arr[u, v] is a 125 length vector now:
Expression(AFFINE, UNKNOWN, (125,))
answered Mar 9 at 21:43
Jacques KvamJacques Kvam
1,29011222
It seems that cvxpy doesn't support more than 2 dimensions, which is what you are doing when you are indexing arr with u, v and w.
As an alternative you can simply reshape those indexing variables so they are 1 dimensional:
u, v, w = [x.reshape(-1) for x in np.meshgrid(range(V), range(V), range(V))]
Then this works just fine:
constraints = [arr[u, v] + arr[v, w] + arr[u, w] <= 1]
arr[u, v] is a 125 length vector now:
Expression(AFFINE, UNKNOWN, (125,))
answered Mar 9 at 21:43
Jacques KvamJacques Kvam
1,29011222
answered Mar 9 at 21:43
Jacques KvamJacques Kvam
1,29011222
answered Mar 9 at 21:43
Jacques KvamJacques Kvam
1,29011222
answered Mar 9 at 21:43
Jacques KvamJacques Kvam
1,29011222
1,29011222
add a comment |
add a comment |
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