convex vector optimization

基本解释

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，由于支持向量机算法是一个凸二次优化问题，能够保证找到的极值解就是全局最优解。
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.
在此基础上，得到了向量目标函数既是似凸又是拟凸的多目标最优化问题的G-恰当有效解集是连通的结论。
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.
在部分生成锥内部凸-锥-凸映射下，得到了既有等式约束又有不等式约束的向量优化问题弱有效解的最优性必要条件。