梯度法对许多非线性问题均具有较好的性能，计算目标函数可以使用新的共轭变量法，有望显著提高寻优效率。
Generally, to nonlinear problem, gradient-based method is faster than simple method, and may improve efficiency due to using conjugate variables to calculate object function value.
海森堡测不准原理是一项基本自然法则，可运用到计算其它变量的共轭对上，比如能量和时间。
The Heisenberg uncertainty principle is a fundamental principle of nature, and applies to the calculations of other conjugate pairs of variables, such as energy and time.
无约束优化问题的共轭梯度路径构造的思想启迪我们用其来解带线性等式约束和有界变量约束的优化问题。
The idea of conjugate gradient path in unconstrained optimization irradiates us to use this method for solving the linear equality optimization subject to bounds on variables.
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