机器学习要用到的基础知识

1.Cauchy–Schwarz inequality

The Cauchy–Schwarz inequality states that for all vectors u and  v of an inner product space it is true that

 {\displaystyle |\langle \mathbf {u} ,\mathbf {v} \rangle |^{2}\leq \langle \mathbf {u} ,\mathbf {u} \rangle \cdot \langle \mathbf {v} ,\mathbf {v} \rangle ,}

 \langle \cdot ,\cdot \rangle is the inner product.

Equivalently, by taking the square root of both sides, and referring to the norms of the vectors, the inequality is written as:

{\displaystyle |\langle \mathbf {u} ,\mathbf {v} \rangle |\leq \|\mathbf {u} \|\|\mathbf {v} \|.}

Moreover, the two sides are equal if and only if \mathbf {u} and \mathbf {v} are linearly dependent (meaning they are parallel: one of the vector's magnitudes is zero, or one is a scalar multiple of the other).

If  {\displaystyle u_{1},\ldots ,u_{n}\in \mathbb {C} } and  {\displaystyle v_{1},\ldots ,v_{n}\in \mathbb {C} }, and the inner product is the standard complex inner product, then the inequality may be restated more explicitly as follows (where the bar notation is used for complex conjugation):

{\displaystyle |u_{1}{\bar {v}}_{1}+\cdots +u_{n}{\bar {v}}_{n}|^{2}\leq (|u_{1}|^{2}+\cdots +|u_{n}|^{2})(|v_{1}|^{2}+\cdots +|v_{n}|^{2})}

or {\displaystyle \left|\sum _{i=1}^{n}u_{i}{\bar {v}}_{i}\right|^{2}\leq \sum _{j=1}^{n}|u_{j}|^{2}\sum _{k=1}^{n}|v_{k}|^{2}.}

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转载自blog.csdn.net/zpainter/article/details/86488332