问题描述
我想使用 scipy.optimize.minimize 求解 (2+x_1)/(1+x_2)-3x_1+4x_3 的最小值，x_1、x_2 和 x_3 的约束范围在 0.1 到0.9.我的代码如下：
rest = [[0.1,0.9],[0.1,0.9]]
cons = [{'type': 'ineq','fun': lambda x: x[i]-0.1 if j == 0 else lambda x: 0.9-x[i]} for i in range(3) for j in range(2)]
# cons = [{'type': 'ineq','fun': lambda x: x[0]-0.1},# {'type': 'ineq','fun': lambda x: 0.9-x[0]},'fun': lambda x: x[1]-0.1},'fun': lambda x: 0.9-x[1]},'fun': lambda x: x[2]-0.1},'fun': lambda x: 0.9-x[2]}]
res2 = minimize(lambda x: (2 + x[0]) / (1 + x[1]) - 3 * x[0] + 4 * x[2],np.array((0.5,0.5,0.5)),method='SLSQP',constraints=cons)
res2.fun,res2.success,res2.x
运行它并报告类型错误：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-7-3caab45922c2> in <module>
7 # {'type': 'ineq',8 # {'type': 'ineq','fun': lambda x: 0.9-x[2]}]
----> 9 res2 = minimize(lambda x: (2 + x[0]) / (1 + x[1]) - 3 * x[0] + 4 * x[2],constraints=cons)
10 res2.fun,res2.x
~\anaconda3\lib\site-packages\scipy\optimize\_minimize.py in minimize(fun,x0,args,method,jac,hess,hessp,bounds,constraints,tol,callback,options)
623 return _minimize_cobyla(fun,**options)
624 elif meth == 'slsqp':
--> 625 return _minimize_slsqp(fun,626 constraints,callback=callback,**options)
627 elif meth == 'trust-constr':
~\anaconda3\lib\site-packages\scipy\optimize\slsqp.py in _minimize_slsqp(func,maxiter,ftol,iprint,disp,eps,finite_diff_rel_step,**unkNown_options)
410 g = append(sf.grad(x),0.0)
411 c = _eval_constraint(x,cons)
--> 412 a = _eval_con_normals(x,cons,la,n,m,meq,mieq)
413
414 while 1:
~\anaconda3\lib\site-packages\scipy\optimize\slsqp.py in _eval_con_normals(x,mieq)
484
485 if cons['ineq']:
--> 486 a_ieq = vstack([con['jac'](x,*con['args'])
487 for con in cons['ineq']])
488 else: # no inequality constraint
~\anaconda3\lib\site-packages\scipy\optimize\slsqp.py in <listcomp>(.0)
484
485 if cons['ineq']:
--> 486 a_ieq = vstack([con['jac'](x,*con['args'])
487 for con in cons['ineq']])
488 else: # no inequality constraint
~\anaconda3\lib\site-packages\scipy\optimize\slsqp.py in cjac(x,*args)
282 rel_step=finite_diff_rel_step)
283 else:
--> 284 return approx_derivative(fun,x,method='2-point',285 abs_step=epsilon,args=args)
286
~\anaconda3\lib\site-packages\scipy\optimize\_numdiff.py in approx_derivative(fun,rel_step,abs_step,f0,sparsity,as_linear_operator,kwargs)
424
425 if sparsity is None:
--> 426 return _dense_difference(fun_wrapped,h,427 use_one_sided,method)
428 else:
~\anaconda3\lib\site-packages\scipy\optimize\_numdiff.py in _dense_difference(fun,use_one_sided,method)
495 x = x0 + h_vecs[i]
496 dx = x[i] - x0[i] # Recompute dx as exactly representable number.
--> 497 df = fun(x) - f0
498 elif method == '3-point' and use_one_sided[i]:
499 x1 = x0 + h_vecs[i]
TypeError: unsupported operand type(s) for -: 'function' and 'function'
我不知道为什么未注释的 cons 和注释的 cons 是不等价的。谢谢回答！
解决方法
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