2022年4月 《算法導論》第4版發布！！ 用的是金色封面。同時第4版給大家準備了一份極為豐厚的禮物，也就是全書算法的Python代碼，并附有測試。 ? ? 還有第三版的官方MIT出版的教師手冊（也就是習題答案）！！

算法導論第四版修訂內容主要包括下面：
The specific changes for the fourth edition include the following:

We renamed Chapter 3 and added a section giving an overview of asymptotic notation before delving into the formal definitions.

Chapter 4 underwent substantial changes to improve its mathematical foundation and make it more robust and intuitive. The notion of an algorithmic recurrence was introduced, and the topic of ignoring floors and ceilings in recurrences was addressed more rigorously. The second case of the master theorem incorporates polylogarithmic factors, and a rigorous proof of a “continuous” version of the master theorem is now provided. We also present the powerful and general Akra-Bazzi method (without proof).

The deterministic order-statistic algorithm in Chapter 9 is slightly different, and the analyses of both the randomized and deterministic order-statistic algorithms have been revamped.

In addition to stacks and queues, Section 10.1 discusses ways to store arrays and matrices.

Chapter 11 on hash tables includes a modern treatment of hash functions. It also emphasizes linear probing as an efficient method for resolving collisions when the underlying hardware implements caching to favor local searches.

To replace the sections on matroids in Chapter 15, we converted a problem in the third edition about offline caching into a full section.

Section 16.4 now contains a more intuitive explanation of the potential functions to analyze table doubling and halving.

Chapter 17 on augmenting data structures was relocated from Part III to Part V, reflecting[…]

“algorithmic ideas. The chapter now focuses on the key aspect of how to model problems as linear programs, along with the essential duality property of linear programming.

Section 32.5 adds to the chapter on string matching the simple, yet powerful, structure of suffix arrays.

Chapter 33, on machine learning, is the third new chapter. It introduces several basic methods used in machine learning: clustering to group similar items together, weighted-majority algorithms, and gradient descent to find the minimizer of a function.

Section 34.5.6 summarizes strategies for polynomial-time reductions to show that problems are NP-hard.

The proof of the approximation algorithm for the set-covering problem in Section 35.3 has been revised.
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版權聲明：本文為CSDN博主「shurenjob_」的原創文章，遵循CC 4.0 BY-SA版權協議，轉載請附上原文出處鏈接及本聲明。
原文鏈接：https://blog.csdn.net/shurenjob_/article/details/127611245

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