convex vector optimization

基本解釋

Because the algorithm of support vector machine is a convex quadratic optimization problem, the local optimal solution is certainly the global optimal one.
并且，由于支持向量機(jī)算法是一個(gè)凸二次優(yōu)化問題，能夠保證找到的極值解就是全局最優(yōu)解。
On the conditions that vector objective function is like-convex and quasi-convex, we obtain the connectedness of G-proper efficient solution set of the multiobjective optimization problem.
在此基礎(chǔ)上，得到了向量目標(biāo)函數(shù)既是似凸又是擬凸的多目標(biāo)最優(yōu)化問題的G-恰當(dāng)有效解集是連通的結(jié)論。
Under the conditions of Partial ic-convex like Maps, optimality necessary conditions of weak efficient solutions for vector optimization problems with equality and inequality constraints are obtained.
在部分生成錐內(nèi)部凸-錐-凸映射下，得到了既有等式約束又有不等式約束的向量?jī)?yōu)化問題弱有效解的最優(yōu)性必要條件。