scipy.optimize.minimizeã«ãŠã‘ã‚‹constraintsオプションã®ä¸Žãˆæ–¹ã¨æ„味ã«ã¤ã„㦠- QiitaDeleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article?
minimize — SciPy v1.14.1 Manual
minimize# scipy.optimize.minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None)[source]# Minimization of scalar function of one or more variables. Parameters: funcallableThe objective function to be minimized. where x is a 1-D array with shape (n,) and args is a tuple of the fixed parameters needed to completely
sec06.dvi
6 6.1 f(x) = 0 (6.1) x = a f(x) (6.1) f(x) = 0 |f(x)| ≤ δ (6.2) x = a δ 6.2 [ab] f(x) = 0 x = xs xl = a xr = b f(xl) f(xr) sign(f(a)), sign(f(b)) sign(f(a)) �= sign(f(b)) a b 1 2 3 4 a b 2 1 3 6.1: ( ) ( ) xm = (xl + xr)/2 f(xm) STEP-1: xm ↠(xr + xl)/2 |f(x�)| ≤ δ xm STEP-2: sign(f(xm)) = sign(f(a)) [xm xr] xl ↠xm STEP-1 STEP-3: sign(f(xm)) �= sign(f(a)) [xl xm] xr ↠xm STEP-1 (bisection method)
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