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Globally and superlinearly convergent algorithm for minimizing a normal merit function

机译:全局和超线性收敛算法,用于最小化正常价值函数

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In this paper we present two new concepts related to the solution of systems of nonsmooth equations (NE) and variational inequalities (VI). The first concept is that of a normal merit function, which summarizes the simple basic properties shared by various known merit functions. In general, normal merit functions are locally Lipschitz, but not differentiable. The second concept is that of a Newtonian operator, whose values generalize the concept of the Hessian for normal merit functions. These two concepts are then used to generalize the nonsmooth Newton method for solving the equation del f(x) = 0, where f is a normal merit function with f is an element of C-1, to the case where f is only locally Lipschitz and the set-valued inclusion 0 is an element of partial derivative f(x) needs to be solved. Combining the resulting generalized Newton method with certain first-order methods, we obtain a globally and superlinearly convergent algorithm for minimizing normal merit functions. [References: 29]
机译:在本文中,我们提出了两个与非光滑方程组(NE)和变分不等式(VI)解有关的新概念。第一个概念是普通价值函数的概念,它概括了各种已知价值函数共享的简单基本属性。通常,正常的功绩函数在局部为Lipschitz,但不可区分。第二个概念是牛顿算子的概念,其值概括了正常价值函数的黑森州概念。然后,将这两个概念用于推广求解方程del f(x)= 0的非光滑牛顿法,其中f是一个标准的价值函数,f是C-1的一个元素,在这种情况下,f仅是局部Lipschitz集值包含0是偏导数f(x)的元素,需要求解。将所得的广义牛顿法与某些一阶方法相结合,我们获得了一个全局和超线性收敛算法,用于最小化正常价值函数。 [参考:29]

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