Question: I faced some issues when doing constrainted optmiziation. Use CVXPY Variables in Optimization: I'll set the unknown values (NaNs) to be part of the CVXPY optimization variable. import cvxpy as cp import numpy as np def optimize_x_simple(A, x_values): # Convert the list to a numpy array x_values = np.array(x_values, dtype=float) # Identify which entries are NaN (unknown) known_mask = np.isnan(x_values) # Set up CVXPY variables for the unknowns x_unknown = cp.Variable(np.sum(known_mask), nonneg=True) # only unknown values are optimized # Replace known values (non-NaN) in x with their values x_full = np.copy(x_values) # The indices of the unknown values unknown_indices = np.where(known_mask)[0] # Construct the full vector with CVXPY variables for unknowns for idx, unknown_idx in enumerate(unknown_indices): x_full[unknown_idx] = x_unknown[idx] # Compute b = A @ x, where x is the full vector b = A @ x_full # Define the constraints: 1.0 <= b_i <= 2.0 for each entry in b constraints = [b >= 1.0, b <= 2.0] # Define the objective function: minimize the sum of unknown values (just an example) objective = cp.Minimize(cp.sum(x_unknown)) # Create the optimization problem problem = cp.Problem(objective, constraints) # Solve the optimization problem problem.solve() if problem.status != cp.OPTIMAL: raise ValueError(f"Optimization failed: {problem.status}") # Replace optimized values back into the result x_optimized = np.copy(x_full) for idx, unknown_idx in enumerate(unknown_indices): x_optimized[unknown_idx] = x_unknown.value[idx] return x_optimized, b.value # Example usage: A = np.random.randn(5895, 393) # Replace with your actual A matrix x_example = [0.1, 0.2, np.nan, 0.1, 0.0] # Example input with NaN for unknowns # Call the optimization function x_optimized, b_vector = optimize_x_simple(A, x_example) # Print the optimized x and resulting b print("Optimized x:", x_optimized) print("Result b:", b_vector) # Construct the full vector with CVXPY variables for unknowns for idx, unknown_idx in enumerate(unknown_indices): x_full[unknown_idx] = x_unknown[idx] ValueError: setting an array element with a sequence.  ERROR LOG TypeError Traceback (most recent call last) TypeError: float() argument must be a string or a real number, not 'index' The above exception was the direct cause of the following exception: ValueError Traceback (most recent call last) Cell In[1], line 55 52 x_example = [0.1, 0.2, np.nan, 0.1, 0.0] # Example input with NaN for unknowns 54 # Call the optimization function ---> 55 x_optimized, b_vector = optimize_x_simple(A, x_example) 57 # Print the optimized x and resulting b 58 print("Optimized x:", x_optimized) Cell In[1], line 22, in optimize_x_simple(A, x_values) 20 # Construct the full vector with CVXPY variables for unknowns 21 for idx, unknown_idx in enumerate(unknown_indices): ---> 22 x_full[unknown_idx] = x_unknown[idx] 24 # Compute b = A @ x, where x is the full vector 25 b = A @ x_full ValueError: setting an array element with a sequence.  How do I solve the Above error? I am seeing the TypeERROR AS SHOWN BELOW. First section will be the coding that I m usin. Second section is the error that I am facing. What are the fixes?

Answer: You can't set the element of a numpy array with a cvxpy variable. A workaround is to use a dummy matrix multiplication to inflate your x_unknown to the size of x_full and then add the inflated array to the known values. Additionally, you need to replace the NaNs in x_full with zeros. IDX_unknown2full = np.zeros((len(x_full), x_unknown.shape[0])) for idx, unknown_idx in enumerate(unknown_indices): IDX_unknown2full[unknown_idx, idx] = 1 x_full[np.isnan(x_full)] = 0 b = A @ (x_full + IDX_unknown2full @ x_unknown)