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AuthorSheldon Axler 著, 吴俊达, 何阳 译

The fourth edition of Linear Algebra Done Right contains over 250 new exercises and over 70 new examples, along with several new topics and multiple improvements throughout the book. See page xvi in the English file linked above or page xi in the Chinese file linked above for a list of major improvements and additions in the fourth edition. This best-selling textbook for a second course in linear algebra is aimed at undergraduate math majors and graduate students. The novel approach taken here banishes determinants to the end of the book. The text focuses on the central goal of linear algebra: understanding the structure of linear operators on finite-dimensional vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. No prerequisites are assumed other than the usual demand for suitable mathematical maturity. Thus the text starts by discussing vector spaces, linear independence, span, basis, and dimension. The book then deals with linear maps, eigenvalues, and eigenvectors. Inner product spaces are then introduced, leading to the finite-dimensional spectral theorem and its consequences such as the singular value decomposition. Generalized eigenvectors are then used to provide insight into the structure of a linear operator. Determinants are cleanly introduced via alternating multilinear forms.

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【One-Line Pitch】 The fourth edition of Linear Algebra Done Right contains over 250 new exercises and over 70 new examples, along with … 【Book Arc】 - **Opening (~0%–12%)**: This best-selling textbook for a second course in linear algebra is aimed at undergraduate math majors and graduate students.; 联系不上 请访问下面的网站.欢迎您提出建议、评论 作者)以获得将此书内容进行翻译或用作其 和更正. 他商业用途的授权. 祝您顺利讲授线性代数课程! Sheldon Axler 旧金山州立大学 网… - **Early (~12%–35%)**: , 𝑉𝑚中元素所有可能的和所构 成的集合,记作 𝑉1 · · · 𝑉𝑚.更确切地说, 𝑉1 · · · 𝑉𝑚 {𝑣1 · · · 𝑣𝑚 : 𝑣1 𝑉1, .; 组 (1, 1, 0), (0, 0, 1)是 {(𝑥, 𝑥, 𝑦) F3 : 𝑥, 𝑦 F}的基. (f) 向量组 (1, 1, 0), (1, 0, 1)是 {(𝑥, 𝑦, 𝑧) F3 : 𝑥 𝑦 𝑧 0}的基. (g) 向量组 1, 𝑧, . - **Middle (~35%–65%)**: , 𝑤𝑚是 range𝑇的基.于是对每个 𝑣 𝑉,存在唯一的 𝜑1(𝑣), .; 注 这里 𝑝 (𝑘 ) 表示 𝑝的 𝑘 阶导数,𝑝的 0阶导数应理解为 𝑝本身. 10 设 𝑚是一正整数. (a) 证明 1, 𝑥 5, . - **Late (~65%–88%)**: 为 4的规范正交组,所以 由 6.28可得该组是 F4 的规范正交基. 一般来说,给定 𝑉 的基 𝑒1, .; 过程中的式 (6.43),我们就能得 出,若 𝑝 P2(R),那么(𝜑ˆ(𝑝) ⟨𝑝, 𝑞⟩,其中 1 ˆ 1 ) 1 ( 1 3 ) 𝑞(𝑥) ( cos(𝜋𝑡)d𝑡) ?… - **Ending (~88%–100%)**: dim range𝑇 dim range𝑇 dim range𝑇 𝑇. 证明 (a) 因为 (𝑇 𝑇) 𝑇 (𝑇 ) 𝑇 𝑇, 所以 𝑇 𝑇 是自伴的. 如果 𝑣 𝑉…; , 1) . R3 中的一个平行体. 7.104 可逆算子化平行体为平行体 设 𝑢 𝑉 且 𝑣1, . 【Key Takeaways】 - **This best** (Opening): This best-selling textbook for a second course in linear algebra is aimed at undergraduate math majors and graduate students. - **联系不上 请访问下面的网站.欢迎您提出建议、…** (Opening): 联系不上 请访问下面的网站.欢迎您提出建议、评论 作者)以获得将此书内容进行翻译或用作其 和更正. 他商业用途的授权. 祝您顺利讲授线性代数课程! Sheldon Axler 旧金山州立大学 网… - **, 𝑉𝑚中元素所有可能的和所构 成的集合…** (Early): , 𝑉𝑚中元素所有可能的和所构 成的集合,记作 𝑉1 · · · 𝑉𝑚.更确切地说, 𝑉1 · · · 𝑉𝑚 {𝑣1 · · · 𝑣𝑚 : 𝑣1 𝑉1, . - **组 (1, 1, 0), (0, 0, 1)…** (Early): 组 (1, 1, 0), (0, 0, 1)是 {(𝑥, 𝑥, 𝑦) F3 : 𝑥, 𝑦 F}的基. (f) 向量组 (1, 1, 0), (1, 0, 1)是 {(𝑥, 𝑦, 𝑧) F3 : 𝑥 𝑦 𝑧 0}的基. (g) 向量组 1, 𝑧, . - **). 从 R3 到 R2 的映射 定义线性映…** (Early): ). 从 R3 到 R2 的映射 定义线性映射 𝑇 L(R3,R2)为 𝑇 (𝑥, 𝑦, 𝑧) (2𝑥 𝑦 3𝑧, 7𝑥 5𝑦 6𝑧). 从 F𝑛 到 F𝑚的映射 为推广上个例子,令 𝑚和 𝑛为正整数,并令 𝐴 𝑗 ,𝑘 F( 𝑗 1, . - **设 𝑉 是有限维的** (Early): 设 𝑉 是有限维的,𝑋 是 𝑉 的子空间,𝑌 是𝑊 的有限维子空间.证明:存在 𝑇 L(𝑉,𝑊)使 得 null𝑇 𝑋 且 range𝑇 𝑌,当且仅当 dim 𝑋 di… 【Reading Tips】 - Use Passage locations below to jump into the text and set reading anchors - If this is a brief outline, click Regenerate (top right) for a synthesized guide 【Coverage Limits】 Compressed outline without the model (~32 index chunks). Full structured guide needs AI available.
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. . . . . . . . . . . . . . 120 奇数维的实向量空间上的特征值 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 习题 5B . . . . . . . . . . . . . . . . . . . . . ....
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Excerpt 2
翻译为向量组. ≈ 7𝜋 《线性代数应该这样学》(第四版) Sheldon Axler [著] 吴俊达、何阳 [译] 28 第 2章 有限维向量空间 接下来这条引理是一个绝佳的工具.它是说,给定一个线性相关的向量组,其中就有某个 向量处于排在其之前的向量的张成空间里.进而,我们可从该组中去掉那个向量,而不改变该...
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Excerpt 3
𝑛 矩阵(𝐴 ≠ 0),证明:𝐴 的秩为 1,当且仅当存在 (𝑐1, . . . , 𝑐𝑚) ∈ F𝑚 和 (𝑑1, · · · , 𝑑𝑛) ∈ F𝑛 使得 𝐴 𝑗 ,𝑘 = 𝑐 𝑗𝑑𝑘 对任意 𝑗 = 1, . . . , 𝑚和任意 𝑘 = 1, . . . , 𝑛都...
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Excerpt 4
明 对 𝑚用归纳法.我们欲证的结论当 𝑚 = 1时是成立的,因为若 𝑎1 ≠ 0那么多项式 𝑎0 +𝑎1𝑧 仅有一个零点(等于 −𝑎0/𝑎1).从而,假定 𝑚 > 1且欲证的结论对于 𝑚 − 1情形成立. 如果 𝑝在 F中无零点,那么欲证结论成立,证明完成.于是假设 𝑝有一个零点 𝜆 ∈...
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Excerpt 5
首先必须说明𝑊 上的这两个算子可交换.为此,设 𝑤 ∈ 𝑊.那么存在 𝑎 ∈ C,( 使得 ) ( ) ( ) (𝑆𝑇)𝑤 = 𝑆 𝑃(𝑇𝑤) = 𝑆(𝑇𝑤 − 𝑎𝑣1) = 𝑃 𝑆(𝑇𝑤 − 𝑎𝑣1) = 𝑃 (𝑆𝑇)𝑤 , 其中最后一个等号成立是因为 𝑣1...
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Excerpt 6
𝑉 − dim𝑈. ♡ 《线性代数应该这样学》(第四版) Sheldon Axler [著] 吴俊达、何阳 [译] 184 第 6章 内积空间 在 𝑥接近 0时,泰勒多项式能很好地逼近 sin 𝑥.然而上述图像表明,对于 |𝑥 | > 2,泰勒 多项式就不那么准确了,特别是与式 (6.65)相比.例如,...
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Excerpt 7
dim range𝑇 = dim range𝑇 ∗ = dim range𝑇 ∗𝑇. ♡ 证明 (a) 因为 (𝑇 ∗𝑇)∗ = 𝑇 ∗(𝑇 ∗)∗ = 𝑇 ∗𝑇, 所以 𝑇 ∗𝑇 是自伴的. 如果 𝑣 ∈ 𝑉,那么 ⟨(𝑇 ∗𝑇)𝑣, 𝑣⟩ = ⟨𝑇 ∗(𝑇𝑣), 𝑣...
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Excerpt 8
𝑇 的同时对应于不同特征值 𝜆𝑘 和 𝜆𝑚 的广义特征向量,这会与 8.11相矛盾. 又有 ( ) ( ) (𝑇 − 𝜆𝑘 𝐼)𝑛 (𝑇 − 𝜆𝑚𝐼)𝑛𝑣𝑘 = (𝑇 − 𝜆𝑚𝐼)𝑛 (𝑇 − 𝜆𝑘 𝐼)𝑛𝑣𝑘 = 0. 从而,综合上述两式得 (𝑇 −...
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Publish Year: 2025
Language: Chinese
File Format: PDF
File Size: 3.3 MB
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