The topics covered in this course include : unconstrained optimization methods , constrained optimization methods , convex analysis , lagrangian relaxation , nondifferentiable optimization , and applications in integer programming 這門(mén)課程的主題包括:無(wú)限制最適化方法,限制最適化方法,凸分析,拉格朗日松弛法,不可微分函數(shù)最適化,以及在整數(shù)規(guī)劃上的應(yīng)用。
Taking all the interval number s in the equations as optimal design variables and representing the lower and th e upper bounds of each interval number as the boundary constrain of the variable respectively , the maximum and the minimum of various components of the solution are achieved by using the constrained optimization method 將方程組中的所有區(qū)間數(shù)都作為設(shè)計(jì)變量,區(qū)間量的變化區(qū)間作為相應(yīng)的設(shè)計(jì)變量的邊界約束,運(yùn)用約束優(yōu)化法求出方程組解的各元素的最大值和最小值。
At present , constrained optimization methods may be classified to two classes , one is search method which firstly asserts whether the current point is an optimal point , if the point is not , then we must choose search directions , and along the search direction , find the next iterative point which make the objective function or merit function to decrease 目前的約束優(yōu)化算法可以分成兩大類,一類是搜索算法,這種算法首先判斷當(dāng)前點(diǎn)是否為最優(yōu)點(diǎn),若非最優(yōu)點(diǎn)則要確定搜索方向,然后沿此方向確定一個(gè)使目標(biāo)函數(shù)或評(píng)價(jià)函數(shù)下降的點(diǎn)。這種算法一般為下降算法,如可行方向法、約束變尺度法等。