{"id":176251,"date":"2024-05-08T19:07:20","date_gmt":"2024-05-08T11:07:20","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/176251.html"},"modified":"2024-05-08T19:07:25","modified_gmt":"2024-05-08T11:07:25","slug":"python%e6%80%8e%e4%b9%88%e6%b1%82%e4%b8%80%e4%b8%aa%e5%87%bd%e6%95%b0%e7%9a%84%e6%9c%80%e5%80%bc","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/176251.html","title":{"rendered":"python\u600e\u4e48\u6c42\u4e00\u4e2a\u51fd\u6570\u7684\u6700\u503c"},"content":{"rendered":"<p style=\"text-align:center\"><img decoding=\"async\" src=\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/27055822\/4d4f1b18-663b-451b-ba76-79683b5aedee.webp\" alt=\"python\u600e\u4e48\u6c42\u4e00\u4e2a\u51fd\u6570\u7684\u6700\u503c\" \/><\/p>\n<p><p>Python\u6c42\u89e3\u4e00\u4e2a\u51fd\u6570\u7684\u6700\u503c\u53ef\u4ee5\u91c7\u7528\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u5206\u6790\u6cd5\u3001\u8fed\u4ee3\u6cd5\u3001\u68af\u5ea6\u4e0b\u964d\u6cd5\u3001\u4f7f\u7528\u4f18\u5316\u7b97\u6cd5\u5e93\u5982SciPy\u3002\u5728Python\u4e2d\uff0c\u6700\u5e38\u7528\u7684\u65b9\u6cd5\u662f\u901a\u8fc7SciPy\u5e93\u4e2d\u7684\u4f18\u5316\u6a21\u5757\u8fdb\u884c\u6c42\u89e3\uff0c\u56e0\u4e3a\u8fd9\u4e2a\u5e93\u63d0\u4f9b\u4e86\u529f\u80fd\u5f3a\u5927\u3001\u7528\u6cd5\u7b80\u6d01\u7684\u6700\u4f18\u5316\u51fd\u6570\u3002\u5bf9\u4e8e\u7b80\u5355\u7684\u4e00\u5143\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u5206\u6790\u6cd5\u5148\u6c42\u5bfc\u6570\uff0c\u518d\u627e\u5230\u5bfc\u6570\u4e3a\u96f6\u7684\u70b9\u786e\u5b9a\u6781\u503c\uff1b\u800c\u5bf9\u4e8e\u590d\u6742\u7684\u591a\u5143\u51fd\u6570\u6216\u8005\u4e0d\u5bb9\u6613\u6c42\u5bfc\u7684\u51fd\u6570\uff0c\u53ef\u4ee5\u7528\u8fed\u4ee3\u6cd5\u6216\u68af\u5ea6\u4e0b\u964d\u6cd5\u8fdb\u884c\u6570\u503c\u6c42\u89e3\u3002<\/p>\n<\/p>\n<p><p>\u4f8b\u5982\uff0c\u82e5\u9700\u6c42\u89e3\u51fd\u6570 f(x) = x^2 \u7684\u6700\u5c0f\u503c\uff0c\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u5bfc\u6570\u8bbe\u7f6e\u7b49\u4e8e\u96f6\u89e3\u6790\u6c42\u89e3\u51fa x=0 \u65f6\uff0cf(x)\u53d6\u5f97\u6700\u5c0f\u503c\u3002\u5728Python\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u5229\u7528SciPy\u63d0\u4f9b\u7684optimize\u6a21\u5757\u6765\u5b8c\u6210\u8fd9\u4e2a\u4efb\u52a1\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u8ba8\u8bba\u5982\u6b64\u8fc7\u7a0b\u3002<\/p>\n<\/p>\n<p><h3>\u4e00\u3001\u5bfc\u5165\u5fc5\u8981\u7684\u5e93<\/h3>\n<\/p>\n<p><p>\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u8ba1\u7b97\u6570\u5b66\u4e2d\u5e38\u7528\u7684\u5e93\uff0c\u8fd9\u5bf9\u4e8e\u540e\u7eed\u7684\u6700\u503c\u6c42\u89e3\u8fc7\u7a0b\u662f\u5fc5\u4e0d\u53ef\u5c11\u7684\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">import numpy as np<\/p>\n<p>from scipy import optimize<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e8c\u3001\u5b9a\u4e49\u51fd\u6570<\/h3>\n<\/p>\n<p><p>\u5176\u6b21\uff0c\u5b9a\u4e49\u9700\u8981\u6c42\u89e3\u6700\u503c\u7684\u51fd\u6570\u3002\u51fd\u6570\u53ef\u4ee5\u662f\u9884\u5148\u5b9a\u4e49\u7684\u6570\u5b66\u5f0f\uff0c\u4e5f\u53ef\u4ee5\u662f\u6839\u636e\u95ee\u9898\u573a\u666f\u62bd\u8c61\u51fa\u7684\u6a21\u578b\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">def func(x):<\/p>\n<p>    return x2<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e09\u3001\u5206\u6790\u6cd5<\/h3>\n<\/p>\n<p><p>\u5bf9\u4e8e\u4e00\u4e9b\u80fd\u901a\u8fc7\u6570\u5b66\u65b9\u6cd5\u6c42\u89e3\u7684\u7b80\u5355\u51fd\u6570\uff0c\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7\u6c42\u89e3\u5bfc\u6570\u7684\u65b9\u5f0f\u5f97\u5230\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">def derivative_func(x):<\/p>\n<p>    return 2*x<\/p>\n<h2><strong>\u901a\u8fc7\u8bbe\u5b9a\u5bfc\u6570\u7b49\u4e8e0\uff0c\u89e3\u65b9\u7a0b\u627e\u5230\u51fd\u6570\u7684\u6781\u503c\u70b9\u3002<\/strong><\/h2>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u56db\u3001\u6570\u503c\u4f18\u5316\u6cd5<\/h3>\n<\/p>\n<p><p>\u5bf9\u4e8e\u590d\u6742\u51fd\u6570\uff0c\u6211\u4eec\u901a\u5e38\u4f7f\u7528\u6570\u503c\u65b9\u6cd5\u3002SciPy\u7684\u4f18\u5316\u65b9\u6cd5\u662f\u6700\u5e38\u7528\u7684\u65b9\u5f0f\u4e4b\u4e00\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">result = optimize.minimize(func, x0=0)  # x0\u662f\u521d\u59cb\u731c\u6d4b\u503c<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e94\u3001\u8fed\u4ee3\u6cd5<\/h3>\n<\/p>\n<p><p>\u8fed\u4ee3\u6cd5\u662f\u4e00\u79cd\u5e38\u89c1\u7684\u6570\u503c\u6c42\u89e3\u65b9\u6cd5\uff0c\u5b83\u901a\u8fc7\u4e0d\u65ad\u5730\u8fed\u4ee3\u66f4\u65b0\u53d8\u91cf\uff0c\u9010\u6b65\u903c\u8fd1\u6700\u4f18\u89e3\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\"># \u4f7f\u7528\u725b\u987f\u8fed\u4ee3\u6cd5\u4f5c\u4e3a\u793a\u4f8b<\/p>\n<p>def newton_iteration_method(x0, tol=1e-5):<\/p>\n<p>    x_current = x0<\/p>\n<p>    while True:<\/p>\n<p>        x_next = x_current - func(x_current)\/derivative_func(x_current)<\/p>\n<p>        if abs(x_next - x_current) &lt; tol:<\/p>\n<p>            return x_next<\/p>\n<p>        x_current = x_next<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u516d\u3001\u68af\u5ea6\u4e0b\u964d\u6cd5<\/h3>\n<\/p>\n<p><p>\u68af\u5ea6\u4e0b\u964d\u6cd5\u662f\u6781\u5c0f\u5316\u51fd\u6570\u7684\u4e00\u79cd\u8fed\u4ee3\u65b9\u6cd5\uff0c\u7279\u522b\u9002\u5408\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u5904\u7406\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">def gradient_descent(x0, learning_rate=0.01, tol=1e-5):<\/p>\n<p>    x_current = x0<\/p>\n<p>    while True:<\/p>\n<p>        gradient = derivative_func(x_current)<\/p>\n<p>        x_next = x_current - learning_rate * gradient<\/p>\n<p>        if abs(func(x_next) - func(x_current)) &lt; tol:<\/p>\n<p>            break<\/p>\n<p>        x_current = x_next<\/p>\n<p>    return x_current<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><h3>\u4e03\u3001\u4f7f\u7528SciPy\u5e93\u7684\u4f18\u5316\u51fd\u6570<\/h3>\n<\/p>\n<p><p>SciPy\u5e93\u4e2d\u6709\u591a\u79cd\u4f18\u5316\u7b97\u6cd5\u53ef\u4f9b\u9009\u62e9\uff0c\u53ef\u4ee5\u5e94\u5bf9\u5404\u79cd\u7c7b\u578b\u7684\u6700\u4f18\u5316\u95ee\u9898\u3002<\/p>\n<\/p>\n<p><pre><code class=\"language-python\">result = optimize.minimize(func, x0=0)  # minimize\u51fd\u6570\u6c42\u53d6\u4e86\u51fd\u6570\u7684\u5168\u5c40\u6700\u5c0f\u503c<\/p>\n<p><\/code><\/pre>\n<\/p>\n<p><p>\u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0cPython\u80fd\u591f\u6709\u6548\u5730\u6c42\u89e3\u51fd\u6570\u7684\u6700\u503c\u3002\u5728\u5b9e\u8df5\u4e2d\uff0c\u6839\u636e\u95ee\u9898\u7684\u5177\u4f53\u60c5\u51b5\u9009\u62e9\u5408\u9002\u7684\u65b9\u6cd5\u663e\u5f97\u5c24\u4e3a\u91cd\u8981\u3002\u4f8b\u5982\uff0c\u68af\u5ea6\u4e0b\u964d\u6cd5\u9002\u7528\u4e8e\u6c42\u89e3\u5927\u89c4\u6a21\u95ee\u9898\u7684\u5c40\u90e8\u6700\u4f18\u89e3\uff0c\u800cSciPy\u5e93\u7684\u4f18\u5316\u51fd\u6570\u901a\u5e38\u7528\u4e8e\u6c42\u89e3\u5168\u5c40\u6700\u4f18\u89e3\u3002\u91cd\u70b9\u5728\u4e8e\u638c\u63e1\u591a\u79cd\u65b9\u6cd5\uff0c\u7075\u6d3b\u8fd0\u7528\u5230\u5b9e\u9645\u95ee\u9898\u4e2d\u53bb\u3002<\/p>\n<\/p>\n<h2><strong>\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n<p><strong>\u5982\u4f55\u7528Python\u6c42\u4e00\u4e2a\u51fd\u6570\u7684\u6700\u5927\u503c\u548c\u6700\u5c0f\u503c\uff1f<\/strong><\/p>\n<ol>\n<li>\n<p><strong>\u5982\u4f55\u7528Python\u7f16\u5199\u4ee3\u7801\u6765\u6c42\u51fd\u6570\u7684\u6700\u5927\u503c\uff1f<\/strong><br \/>\n\u4f60\u53ef\u4ee5\u4f7f\u7528scipy\u5e93\u4e2d\u7684optimize\u6a21\u5757\u6765\u5b9e\u73b0\u8fd9\u4e2a\u4efb\u52a1\u3002\u9700\u8981\u5148\u5b9a\u4e49\u4e00\u4e2a\u5f85\u6c42\u89e3\u7684\u51fd\u6570\uff0c\u7136\u540e\u4f7f\u7528<code>scipy.optimize<\/code>\u4e2d\u7684<code>minimize<\/code>\u51fd\u6570\u6765\u627e\u5230\u51fd\u6570\u7684\u6700\u5927\u503c\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\uff0c\u5b9a\u4e49\u51fd\u6570\uff0c\u8bbe\u7f6e\u53c2\u6570\u5e76\u8c03\u7528<code>minimize<\/code>\u51fd\u6570\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5982\u4f55\u901a\u8fc7\u7ed8\u5236\u56fe\u5f62\u6765\u627e\u5230\u51fd\u6570\u7684\u6700\u5927\u503c\u548c\u6700\u5c0f\u503c\uff1f<\/strong><br \/>\n\u4f60\u53ef\u4ee5\u4f7f\u7528matplotlib\u5e93\u6765\u7ed8\u5236\u51fd\u6570\u7684\u56fe\u5f62\uff0c\u5e76\u901a\u8fc7\u67e5\u770b\u56fe\u5f62\u6765\u627e\u5230\u51fd\u6570\u7684\u6700\u5927\u503c\u548c\u6700\u5c0f\u503c\u3002\u9996\u5148\uff0c\u786e\u5b9ax\u8f74\u7684\u53d6\u503c\u8303\u56f4\uff0c\u7136\u540e\u8ba1\u7b97\u5bf9\u5e94\u7684y\u503c\uff0c\u4f7f\u7528<code>plt.plot<\/code>\u51fd\u6570\u5c06x\u548cy\u503c\u7ed8\u5236\u6210\u66f2\u7ebf\u56fe\u3002\u6700\u540e\uff0c\u4f7f\u7528<code>plt.ylim<\/code>\u51fd\u6570\u8bbe\u7f6ey\u8f74\u7684\u53d6\u503c\u8303\u56f4\uff0c\u7136\u540e\u4f7f\u7528<code>plt.show<\/code>\u51fd\u6570\u663e\u793a\u56fe\u5f62\u3002\u901a\u8fc7\u89c2\u5bdf\u66f2\u7ebf\u7684\u6781\u503c\u70b9\u6765\u627e\u5230\u51fd\u6570\u7684\u6700\u5927\u503c\u548c\u6700\u5c0f\u503c\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5982\u4f55\u7528Python\u7f16\u5199\u4ee3\u7801\u6765\u6c42\u51fd\u6570\u7684\u6700\u503c\uff0c\u5e76\u8003\u8651\u7ea6\u675f\u6761\u4ef6\uff1f<\/strong><br \/>\n\u5982\u679c\u4f60\u9700\u8981\u5728\u6700\u503c\u6c42\u89e3\u65f6\u8003\u8651\u7ea6\u675f\u6761\u4ef6\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528scipy\u5e93\u4e2d\u7684optimize\u6a21\u5757\u4e2d\u7684<code>minimize<\/code>\u51fd\u6570\uff0c\u5e76\u5728\u51fd\u6570\u4e2d\u8bbe\u7f6e\u7ea6\u675f\u6761\u4ef6\u3002\u5177\u4f53\u6b65\u9aa4\u5305\u62ec\u5bfc\u5165\u5fc5\u8981\u7684\u5e93\uff0c\u5b9a\u4e49\u5e26\u6709\u7ea6\u675f\u6761\u4ef6\u7684\u51fd\u6570\uff0c\u8bbe\u7f6e\u53c2\u6570\u5e76\u8c03\u7528<code>minimize<\/code>\u51fd\u6570\u3002\u901a\u8fc7\u8bbe\u7f6e\u7ea6\u675f\u6761\u4ef6\uff0c\u53ef\u4ee5\u627e\u5230\u6ee1\u8db3\u7ea6\u675f\u6761\u4ef6\u7684\u51fd\u6570\u7684\u6700\u5927\u503c\u6216\u6700\u5c0f\u503c\u3002<\/p>\n<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"Python\u6c42\u89e3\u4e00\u4e2a\u51fd\u6570\u7684\u6700\u503c\u53ef\u4ee5\u91c7\u7528\u591a\u79cd\u65b9\u6cd5\uff0c\u5305\u62ec\u5206\u6790\u6cd5\u3001\u8fed\u4ee3\u6cd5\u3001\u68af\u5ea6\u4e0b\u964d\u6cd5\u3001\u4f7f\u7528\u4f18\u5316\u7b97\u6cd5\u5e93\u5982SciPy\u3002\u5728 [&hellip;]","protected":false},"author":3,"featured_media":176260,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/176251"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=176251"}],"version-history":[{"count":0,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/176251\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/176260"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=176251"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=176251"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=176251"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}