<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"><channel><title>MagicLen</title><link>https://magiclen.org/tag/fibonacci-sequence/feed</link><description>多元化原創文章，內容包羅萬象，有程式語言、網站應用、軟體介紹、硬體介紹、作業系統、旅行遊記、專題採訪、益智問題、文學創作，以及作者們的知識分享和作品分享。</description><language>zh-TW</language><lastBuildDate>Sat, 11 Apr 2026 13:06:01 +0000</lastBuildDate><sy:updatePeriod>hourly</sy:updatePeriod><sy:updateFrequency>8</sy:updateFrequency><sy:updateBase>1970-01-01T00:00+00:00</sy:updateBase><item><title>用動態規劃解決問題：基本觀念(有重疊子問題的問題)</title><link>https://magiclen.org/dynamic-programming-basic</link><description><![CDATA[    <img src="https://magiclen.org/wp-content/uploads/2020/04/technology-1283624_1920-220x162.jpg" srcset="https://magiclen.org/wp-content/uploads/2020/04/technology-1283624_1920-220x162.jpg 1x, https://magiclen.org/wp-content/uploads/2020/04/technology-1283624_1920-440x324.jpg 2x, https://magiclen.org/wp-content/uploads/2020/04/technology-1283624_1920-660x486.jpg 3x" alt="">
動態規劃(Dynamic Programming，簡稱DP)是一種解決問題的技巧，主要被用來優化那些「記不住自己過去曾解出來的答案所以只好重複再解」的演算法，讓它們可以「記憶」已經找出來的答案，從而不斷利用，以大大降低時間複雜度(從指數級降到線性)。]]></description><category>研究分享</category><category>Java</category><category>Rust</category><category>演算法</category><category>Go</category><category>JavaScript</category><category>Java</category><category>JavaScript</category><category>Rust</category><category>TypeScript</category><category>費氏數列</category><category>動態規劃</category><category>Go</category><guid>https://magiclen.org/dynamic-programming-basic</guid><pubDate>Tue, 21 Jul 2020 12:00:16 +0800</pubDate><dc:creator>Magic Len</dc:creator></item><item><title>如何將遞迴函數改成迭代函數？</title><link>https://magiclen.org/recursive-to-iterative</link><description><![CDATA[    <img src="https://magiclen.org/wp-content/uploads/2020/04/mathematics-1509559_1920-220x162.jpg" srcset="https://magiclen.org/wp-content/uploads/2020/04/mathematics-1509559_1920-220x162.jpg 1x, https://magiclen.org/wp-content/uploads/2020/04/mathematics-1509559_1920-440x324.jpg 2x, https://magiclen.org/wp-content/uploads/2020/04/mathematics-1509559_1920-660x486.jpg 3x" alt="">
遞迴(Recursive)函數是在執行的過程又會直接或間接地呼叫自己本身的函數。通常透過遞迴函數可以快速地驗證我們的演算法，用簡短的程式碼處理複雜的問題，但是函數在呼叫時需要建立新的堆疊框(Stack Frame)，除了會需要額外的開支(Overhead)之外，如果在函數中呼叫函數，而這函數又會呼叫函數，持續下去，很容易就會造成堆疊溢出(Stack Overflow)。雖然有些程式語言的編譯器會做...]]></description><category>研究分享</category><category>Java</category><category>Rust</category><category>Go</category><category>JavaScript</category><category>Java</category><category>JavaScript</category><category>Rust</category><category>二元搜尋</category><category>TypeScript</category><category>費氏數列</category><category>階乘</category><category>Go</category><guid>https://magiclen.org/recursive-to-iterative</guid><pubDate>Thu, 11 Jun 2020 12:00:44 +0800</pubDate><dc:creator>Magic Len</dc:creator></item><item><title>費氏搜尋(Fibonacci Search)演算法，運用費氏數列的搜尋演算法</title><link>https://magiclen.org/fibonacci-search</link><description><![CDATA[    <img src="https://magiclen.org/wp-content/uploads/2016/04/Searchicons-search-blogs-512-220x162.png" srcset="https://magiclen.org/wp-content/uploads/2016/04/Searchicons-search-blogs-512-220x162.png 1x, https://magiclen.org/wp-content/uploads/2016/04/Searchicons-search-blogs-512-440x324.png 2x, https://magiclen.org/wp-content/uploads/2016/04/Searchicons-search-blogs-512-660x486.png 3x" alt="">
費氏搜尋(Fibonacci Search)演算法有點像是二元搜尋(Binary Search)演算法，同樣是在一個已排序好的陣列中搜尋元素，但是它在移動陣列索引值時是去參考費氏數列(Fibonacci Sequence)，而不是像二元搜尋法那樣總是去取索引的中間值。也由於費氏搜尋法在移動陣列索引值時只需要進行加減運算，不需乘、除法，因此它適合被用在不擅長處理乘、除法的CPU上。]]></description><category>研究分享</category><category>Java</category><category>Rust</category><category>演算法</category><category>Go</category><category>JavaScript</category><category>Java</category><category>JavaScript</category><category>Rust</category><category>搜尋演算法</category><category>二元搜尋</category><category>TypeScript</category><category>費氏數列</category><category>費氏搜尋</category><category>Go</category><guid>https://magiclen.org/fibonacci-search</guid><pubDate>Thu, 28 May 2020 12:00:15 +0800</pubDate><dc:creator>Magic Len</dc:creator></item></channel></rss>