WebJul 6, 2024 · Problem: Rank(A) < p or Rank([P; A; G]) < n #15. gnawJ opened this issue Jul 6, 2024 · 3 comments Comments. Copy link gnawJ commented Jul 6, 2024. ... ValueError: Rank(A) < p or Rank([P; A; G]) < n. Thx! The text was updated successfully, but these errors were encountered: All reactions. Copy link I'm trying to use cvxpy (and hence cvxopt) to model optimal power flow in a relatively simple network with 28 nodes and 37 lines, but getting a "Rank (A) < p or Rank ( [G; A]) < n" error. (Using the same code, I can find the optimal solution for a much simpler network with 4 nodes and 4 lines.)
Problem: Rank(A) < p or Rank([P; A; G]) < n #15 - Github
WebJul 7, 2024 · ValueError: Rank (A) < p or Rank ( [P; A; G]) < n The issue arises from the ‘optimal_portfolio’ function. I actually copy and pasted someone elses completed code, and it still wouldn’t work. Could it just be my computer? Here is the function: def optimal_portfolio (returns): n = returns.shape [1] WebMar 9, 2024 · aima-python Python code for the book Artificial Intelligence: A Modern Approach. You can use this in conjunction with a course on AI, or for study on your own. ... ValueError: Rank(A) < p or Rank([P; A; G]) < n FAILED tests/test_learning4e.py::test_svc - ValueError: Rank(A) < p or Rank([P; A; G]) < n. opened Feb 3, 2024 by acsanden 0. Open ... tab to wonderful world jerry garcia
S.O.S Capstone Analyzing Financial Data with Python …
WebRank assumptions We assume that rank(A) = p, rank P AT GT = n where p is the row dimension of A and n is the dimension of x. This is equivalent to assuming that the matrix P AT GT A 0 0 G 0 −Q is nonsingular for any positive definite Q. If rank(A) < p, then either the equality constraints in the primal problem are inconsistent (if b ∈ WebAug 1, 2024 · You are initially generating P as a matrix of random numbers: sometimes P ′ + P + I will be positive semi-definite, but other times it will not. The likelihood is you've run your code and been unlucky that P does not meet this criterion. (It is possible to be lucky: if I set np.random.seed (123) first, then your code runs without error.) WebR=pT w R = p T w where $R$ is the expected return, $p^T$ is the transpose of the vector for the mean returns for each time series and w is the weight vector of the portfolio. $p$ is a Nx1 column vector, so $p^T$ turns into a 1xN row vector which can be multiplied with the Nx1 weight (column) vector w to give a scalar result. tab to window/popup - keyboard shortcut