Optimization Techniques

gradient.py

from autograd import grad
import autograd.numpy as anp

def objective():
  return 

if __name__ == "__main__":
  W        = np.random.normal(0, 1,X_train.shape[1]) 
  gradient = grad(objective)
  W        -= gradient(W, X_11, X_12, X_21, X_22) * LR

optimize_basic.py

def scalar1(x):
    return np.sin(x)*np.exp(-0.1*(x-0.6)**2)

if __name__ == "__main__":
  result = optimize.minimize_scalar(scalar1)
  print (result)
  
  
def objective(x):
    x1 = x[0]
    x2 = x[1]
    x3 = x[2]
    x4 = x[3]
    return x1*x4*(x1+x2+x3)+x3

def constraint1(x):
    return x[0]*x[1]*x[2]*x[3] - 25.0

def constraint2(x):
    sum_sq = 40.0
    for i in range(4):
        sum_sq = sum_sq - x[i]**2
    return sum_sq

if __name__ == "__main__":
    # MULTI-VARIATE OPTIMIZATION 
    x0 = [1,5,5,1]
    print (' - x0_obj : ', objective(x0))
    
    b = (1.0, 5.0)
    bnds = (b,b,b,b)
    con1 = {'type' : 'ineq', 'fun':constraint1}
    con2 = {'type' : 'eq'  , 'fun':constraint2}
    cons = [con1, con2]
    
    sol = optimize.minimize(objective, x0, method='SLSQP', \
                           bounds=bnds, constraints=cons)
    print (sol)
    print (constraint1(sol.x))
    print (constraint2(sol.x))