[Solved] Why does this code raise an error?from scipy.optimize import minimize obj = lambda x: x[0]**2 cons = {'type': 'ineq', 'fun': lambda x: x[0] - 1} result = minimize(obj, 0, constraints=cons) | SciPy

SciPy - Advanced Optimization

Why does this code raise an error?

from scipy.optimize import minimize

obj = lambda x: x[0]**2
cons = {'type': 'ineq', 'fun': lambda x: x[0] - 1}
result = minimize(obj, 0, constraints=cons)

AInitial guess must be an array-like, not a scalar.

BConstraint function must return boolean, not scalar.

CObjective function must have two variables.

DConstraint dictionary must be a list.

Step-by-Step Solution

Solution:

Step 1: Check the initial guess type
Initial guess should be array-like (e.g., list or numpy array), not a scalar.
Step 2: Identify the error cause
Passing 0 (scalar) causes dimension mismatch error in minimize.
Final Answer:
Initial guess must be an array-like, not a scalar. -> Option A
Quick Check:
Initial guess must be array-like [OK]

Quick Trick: Initial guess must be list or array [OK]

Common Mistakes:

Passing scalar instead of array for initial guess
Expecting boolean return from constraint
Assuming objective needs two variables

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Why does this code raise an error?

Step 1: Check the initial guess type

Step 2: Identify the error cause

Final Answer:

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