数据结构与算法分析 C语言描述 (韦斯 (Mark Allen Weiss)) (Z-Library)

【One-Line Pitch】 A rigorous, C-centered tour of data structures and algorithm analysis that teaches you to reason about running time while implementing real abstract data types. Best for readers with intermediate programming and some discrete-math background who want a textbook-grade foundation rather than a quick interview cram. 【Book Arc】 - **Opening (~0%–15%)**: Establishes the dual agenda — organizing large amounts of data and estimating algorithm running time — and frames abstract data types as the design lens for everything that follows. - **Early (~15%–35%)**: Builds the core toolkit (lists, stacks, queues, trees, hashing, heaps) through C implementations, with efficiency and performance analysis attached to each structure. - **Middle (~35%–55%)**: Moves into sorting and the analysis of average-case behavior, including updated results on heapsort's average-case analysis. - **Late (~55%–80%)**: Introduces advanced structures and their implementations — red-black trees, top-down splay trees, treaps, k-d trees, pairing heaps, Fibonacci heaps, skew heaps, binomial queues, skip lists, and splay trees. - **Ending (~80%–100%)**: Dedicates a chapter to algorithm design techniques (greedy, divide-and-conquer, dynamic programming, randomized, backtracking) and a chapter to amortized analysis that revisits the advanced structures introduced earlier. 【Key Takeaways】 - **Abstract data types are the organizing idea** (Early): the book stresses separating interface from implementation, so C code illustrates a concept rather than defining it. - **Efficiency is analyzed, not assumed** (Early): running time and performance estimation are treated as first-class skills, tied directly to the structures being built. - **Advanced structures get real implementations** (Late): red-black trees, top-down splay trees, treaps, k-d trees, and pairing heaps are covered with code, not just descriptions. - **Amortized analysis is its own discipline** (Late): a dedicated chapter applies amortized reasoning to the advanced structures, which is where many self-taught readers have gaps. - **Algorithm design techniques are consolidated** (Ending): greedy, divide-and-conquer, dynamic programming, randomized, and backtracking approaches are gathered into one chapter for comparison. - **Modern structures are included alongside classics** (Late): Fibonacci heaps, skew heaps, binomial queues, skip lists, and splay trees reflect then-current topics rather than a purely historical syllabus. - **The book targets a course, not a casual read** (Opening): it is positioned for an advanced data structures course or first-year graduate algorithm analysis, assuming intermediate programming and some discrete math. 【Reading Tips】 - Deep-read the analysis sections even when the C code looks familiar; the running-time reasoning is the transferable skill. - Skim implementations you already know, but slow down on the advanced structures (red-black trees, splay trees, treaps, k-d trees) where the code and the analysis reinforce each other. - Treat the amortized analysis chapter as a checkpoint — if it feels hard, revisit the advanced structures before continuing. - Use the algorithm design techniques chapter as a synthesis pass; map each technique back to earlier structures where it appeared. - Keep discrete-math references handy, since the analysis assumes that background. 【Coverage Limits】 The excerpts describe the book's scope, audience, and feature list but do not include chapter titles, page counts, or internal detail, so the arc percentages above are inferred from the stated structure rather than from position markers.