When applying eigenvalue decomposition on the quadratic term matrix in a type of linear equally constrained quadratic programming (EQP), there exists a linear mapping to project optimal solutions between the new EQP formulation where $Q$ is diagonalized and the original formulation. Although such a mapping requires a particular type of equality constraints, it is generalizable to some real problems such as efficient frontier for portfolio allocation and classification of Least Square Support Vector Machines (LSSVM). The established mapping could be potentially useful to explore optimal solutions in subspace, but it is not very clear to the author. This work was inspired by similar work proved on unconstrained formulation discussed earlier in \cite{Tan}, but its current proof is much improved and generalized. To the author's knowledge, very few similar discussion appears in literature.

翻译：在一种线性同样受限制的二次编程(EQP)中,对二次术语矩阵施用电子算术分解法时,存在着一种线性绘图,以预测新的EQP配方与原始配方之间的最佳解决办法,即对Q美元进行分解和原始配方,虽然这种绘图需要特定类型的平等限制,但可广泛应用于某些实际问题,如最低广场支持病媒机(LSSVM)的组合分配和分类的有效前沿。已经建立的绘图可能有益于在亚空间探索最佳解决办法,但作者并不十分清楚。这项工作的灵感来自早些时候在\cite{Tan}中讨论过的关于未受限制的配方的类似工作,但目前的证据已大大改进和普及。据作者所知,文献中也很少出现类似的讨论。