It expects whatever you give it to evaluate to a single number, if it doesn't, Numpy responds that it doesn't know how to set an array element with a sequence. X = np.array() #Fail, can't convert the numpy array to fitĪ numpy array is being created, and numpy doesn't know how to cram multivalued tuples or arrays into single element slots. By trying to cram a numpy array length > 1 into a numpy array element: x = np.array() toarray () returns an ndarray Max Kleiner. toarray () we enlarge it to an array of sequence. ![]() ![]() Hence, you're trying to set an array element with a sequence. x cp.Variable(n) objective cp.Minimize(cp.sumsquares(A x - b)) constraints 0 < x, x < 1 prob cp.Problem(objective, constraints) The optimal objective value is returned by. The value error means we're trying to load a n-element array (sequence) into a single number slot which only has a float. value ( numeric type) A value to assign to the variable. ![]() m 30 n 20 np.ed(1) A np.random.randn(m, n) b np.random.randn(m) Construct the problem. Parameters: shape ( tuple or int) The variable dimensions (0D by default). Numpy.array() #Fail, can't convert a list into a numpyĢ. import cvxpy as cp import numpy as np Problem data. an() #Fail, can't convert a tuple into a numpy Numpy.array() #Fail, can't convert a tuple into a numpy When you pass a python tuple or list to be interpreted as a numpy array element: import numpy It can be thrown under various circumstances.ġ. Means exactly what it says, you're trying to cram a sequence of numbers into a single number slot. I don't think there is a much easier and more intuitive way to express these outer products than with matrix multiplications.The Python ValueError: ValueError: setting an array element with a sequence. 9 Answers Sorted by: 359 Possible reason 1: trying to create a jagged array You may be creating an array from a list that isn't shaped like a multi-dimensional array: numpy.array ( 1, 2, 2, 3, 4) wrong numpy.array ( 1, 2, 2, 3, 4) wrong In these examples, the argument to numpy.array contains sequences of different lengths. The constraints are a little bit more complicated due to the outer products. This part can be written with a summation. The matrix form of the objective is similar to the one we saw in the section on the transportation problem. The vast majority of users will need only. CVXPY has seven types of constraints: non-positive, equality or zero, positive semidefinite, second-order cone, exponential cone, 3-dimensional power cones, and N-dimensional power cones. In this section I'll discuss some modeling issues when implementing a simple transportation model in CVXPY, and compare this to a standard GAMS implementation.Īs an example consider the standard transportation model. A constraint is an equality or inequality that restricts the domain of an optimization problem. It discusses some of underlying ideas of CVXPY and Disciplined Convex Programming. This model is very difficult to deal with in CVXPY.Īll these models are candidates for CVXPY: the models are linear or convex and they only use vectors and matrices. CVXPY does not support sparse variables (only sparse data). This is not very easily expressed in CVXPY. The matrix notation becomes a bit more cumbersome.
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