Householder 的音標(biāo)是["ha????Holder]，基本翻譯為“豪斯霍爾德”。速記技巧可以考慮將每個(gè)音節(jié)盡量縮短，如“豪-書(shū)-豪-爾”，這樣可以方便記憶。

Householder的英文詞源為：

"House" - 指房子，房間，通常與"holder"一起使用，表示持有者或擁有者。

"Holder" - 指持有某物的人，通常用于描述數(shù)學(xué)中的矩陣變換。

Householder的相關(guān)變化形式包括復(fù)數(shù)形式"Householders"，過(guò)去式"Householdsed"和過(guò)去分詞"Householded"。

相關(guān)單詞：

1. "Household" - 指家庭，通常用于描述一個(gè)家庭或家庭成員。

2. "Household items" - 指家庭用品或日常用品。

3. "Household budget" - 指家庭預(yù)算，用于管理家庭財(cái)務(wù)。

4. "Household income" - 指家庭收入，通常用于描述家庭成員的總收入。

5. "Household appliances" - 指家用電器，如電視、冰箱、洗衣機(jī)等。

6. "Household goods" - 指家庭財(cái)產(chǎn)或個(gè)人物品。

7. "Householder transformation" - 指一種矩陣變換方法，用于優(yōu)化矩陣運(yùn)算。

8. "Householder vector" - 指一種特殊的向量，用于描述矩陣變換的性質(zhì)。

9. "Household management" - 指家庭管理或財(cái)務(wù)管理，通常用于描述家庭成員如何管理家庭財(cái)務(wù)。

10. "Household income tax" - 指家庭所得稅，用于描述政府對(duì)家庭收入征收的稅收。

以上這些單詞都與Householder有著密切的聯(lián)系，并且可以用來(lái)描述與家庭、財(cái)務(wù)管理、矩陣變換等相關(guān)的概念。

常用短語(yǔ)：

1. householder transformation

2. householder vectors

3. householder reflection

4. householder matrix

5. householder transformation matrix

6. householder transformation coefficients

7. householder transformation matrix inverse

雙語(yǔ)句子：

1. 在執(zhí)行矩陣變換時(shí)，我們經(jīng)常使用Householder變換。

2. 矩陣的Householder變換可以有效地降低維度。

3. 如果你想找到一個(gè)矩陣的特征向量，你可以使用Householder變換。

4. 在優(yōu)化問(wèn)題中，Householder變換是一種常用的方法。

5. 如果你想在計(jì)算機(jī)視覺(jué)中處理圖像，Householder變換是一個(gè)有用的工具。

6. 使用Householder變換，我們可以輕松地找到矩陣的奇異值。

7. 在線性代數(shù)中，Householder變換是一種重要的技術(shù)。

英文小作文：

Title: Householder Transformation in Linear Algebra

Linear algebra is a fundamental topic in mathematics that deals with vectors and matrices. One of the most powerful techniques in linear algebra is the Householder transformation, which is a method used to reduce the dimensionality of a matrix or find its eigenvectors and singular values.

In practical applications, Householder transformation is used in various fields such as matrix manipulation in computer science, optimization problems in engineering, and computer vision for image processing. It is also used in linear regression, principal component analysis, and other statistical methods.

Through Householder transformation, we can easily find the eigenvectors of a matrix, which are the most important characteristics of a matrix that determine its behavior. Additionally, it allows us to construct reflection matrices that can be used for various purposes, such as improving the conditioning of a system or reducing the effects of noise in images.

In summary, Householder transformation is a powerful tool in linear algebra that can be used to solve various problems in different fields. It provides a simple and effective way to manipulate matrices and discover their hidden characteristics.