{"id":232420,"date":"2025-06-12T00:43:38","date_gmt":"2025-06-12T00:43:38","guid":{"rendered":"https:\/\/learnexams.com\/blog\/?p=232420"},"modified":"2025-06-12T00:43:40","modified_gmt":"2025-06-12T00:43:40","slug":"lagrange-interpolation-please-write-a-python-code-using-the-lagrange-interpolation-method-to-find-a-polynomial-to-interpolate-the-following-data-xi-fxi-1-0-56-3-2-68-5-9-01-8-19-23-10-45-74-use-thi","status":"publish","type":"post","link":"https:\/\/www.learnexams.com\/blog\/2025\/06\/12\/lagrange-interpolation-please-write-a-python-code-using-the-lagrange-interpolation-method-to-find-a-polynomial-to-interpolate-the-following-data-xi-fxi-1-0-56-3-2-68-5-9-01-8-19-23-10-45-74-use-thi\/","title":{"rendered":"Lagrange interpolation Please write a python code using the Lagrange interpolation method to find a polynomial to interpolate the following data Xi f(Xi) -1 0.56 3 2.68 5 9.01 8 19.23 10 45.74 use this polynomial to find f(x) at x=7 please upload the code"},"content":{"rendered":"\n<p>Lagrange interpolation Please write a python code using the Lagrange interpolation method to find a polynomial to interpolate the following data Xi f(Xi) -1 0.56 3 2.68 5 9.01 8 19.23 10 45.74 use this polynomial to find f(x) at x=7 please upload the code<\/p>\n\n\n\n<p><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-ast-global-color-0-color\">The Correct Answer and Explanation is:<\/mark><\/strong><\/p>\n\n\n\n<p>Here&#8217;s the Python code implementing Lagrange interpolation for your data:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">pythonCopyEdit<code>from sympy import symbols, simplify, lambdify\n\n# Define symbol\nx = symbols('x')\n\n# Given data points\nX = [-1, 3, 5, 8, 10]\nF = [0.56, 2.68, 9.01, 19.23, 45.74]\n\n# Number of points\nn = len(X)\n\n# Lagrange interpolation polynomial\nlagrange_poly = 0\n\n# Construct the polynomial\nfor i in range(n):\n    term = F[i]\n    for j in range(n):\n        if i != j:\n            term *= (x - X[j]) \/ (X[i] - X[j])\n    lagrange_poly += term\n\n# Simplify polynomial\nlagrange_poly_simplified = simplify(lagrange_poly)\n\n# Create function for evaluation\nf_lagrange = lambdify(x, lagrange_poly_simplified, 'numpy')\n\n# Evaluate at x = 7\nf_at_7 = f_lagrange(7)\n\nprint(\"Lagrange Polynomial:\")\nprint(lagrange_poly_simplified)\nprint(f\"\\nValue at x = 7: {f_at_7}\")\n<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">Output<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Interpolating Polynomial:<\/strong> f(x)=0.0289&#215;4\u22120.4769&#215;3+2.5639&#215;2\u22121.8372x\u22124.3469f(x) = 0.0289x^4 &#8211; 0.4769x^3 + 2.5639x^2 &#8211; 1.8372x &#8211; 4.3469f(x)=0.0289&#215;4\u22120.4769&#215;3+2.5639&#215;2\u22121.8372x\u22124.3469<\/li>\n\n\n\n<li><strong>Value at x = 7:<\/strong> f(7)\u224814.2245f(7) \\approx 14.2245f(7)\u224814.2245<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">Explanation <\/h3>\n\n\n\n<p>Lagrange interpolation is a classical numerical method used to estimate a polynomial that exactly passes through a given set of points. Given n+1n+1n+1 data points (x0,f0),(x1,f1),\u2026,(xn,fn)(x_0, f_0), (x_1, f_1), \\dots, (x_n, f_n)(x0\u200b,f0\u200b),(x1\u200b,f1\u200b),\u2026,(xn\u200b,fn\u200b), the Lagrange polynomial is defined as: L(x)=\u2211i=0nfi\u22c5\u2113i(x)L(x) = \\sum_{i=0}^{n} f_i \\cdot \\ell_i(x)L(x)=i=0\u2211n\u200bfi\u200b\u22c5\u2113i\u200b(x)<\/p>\n\n\n\n<p>where each \u2113i(x)\\ell_i(x)\u2113i\u200b(x) is the Lagrange basis polynomial: \u2113i(x)=\u220fj=0j\u2260inx\u2212xjxi\u2212xj\\ell_i(x) = \\prod_{\\substack{j=0 \\\\ j \\ne i}}^{n} \\frac{x &#8211; x_j}{x_i &#8211; x_j}\u2113i\u200b(x)=j=0j\ue020=i\u200b\u220fn\u200bxi\u200b\u2212xj\u200bx\u2212xj\u200b\u200b<\/p>\n\n\n\n<p>Each basis polynomial \u2113i(x)\\ell_i(x)\u2113i\u200b(x) equals 1 at x=xix = x_ix=xi\u200b and 0 at all other xjx_jxj\u200b, ensuring the polynomial matches the given function values exactly at the specified data points.<\/p>\n\n\n\n<p>In this case, we were given five points:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>xix_ixi\u200b<\/th><th>f(xi)f(x_i)f(xi\u200b)<\/th><\/tr><\/thead><tbody><tr><td>-1<\/td><td>0.56<\/td><\/tr><tr><td>3<\/td><td>2.68<\/td><\/tr><tr><td>5<\/td><td>9.01<\/td><\/tr><tr><td>8<\/td><td>19.23<\/td><\/tr><tr><td>10<\/td><td>45.74<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Using these, we constructed the interpolating polynomial of degree 4. The result is a fourth-degree polynomial explicitly defined in terms of xxx. To estimate the function value at x=7x = 7x=7, we substitute this value into the polynomial, yielding f(7)\u224814.2245f(7) \\approx 14.2245f(7)\u224814.2245.<\/p>\n\n\n\n<p>Lagrange interpolation is accurate for small datasets and smooth functions but can be unstable for large datasets due to Runge\u2019s phenomenon. For such cases, piecewise methods (e.g., splines) may be preferable. However, for moderate-sized datasets like this, Lagrange interpolation is efficient and exact.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/learnexams.com\/blog\/wp-content\/uploads\/2025\/06\/learnexams-banner9-281.jpeg\" alt=\"\" class=\"wp-image-232421\"\/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Lagrange interpolation Please write a python code using the Lagrange interpolation method to find a polynomial to interpolate the following data Xi f(Xi) -1 0.56 3 2.68 5 9.01 8 19.23 10 45.74 use this polynomial to find f(x) at x=7 please upload the code The Correct Answer and Explanation is: Here&#8217;s the Python code [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[25],"tags":[],"class_list":["post-232420","post","type-post","status-publish","format-standard","hentry","category-exams-certification"],"_links":{"self":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/posts\/232420","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/comments?post=232420"}],"version-history":[{"count":0,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/posts\/232420\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/media?parent=232420"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/categories?post=232420"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.learnexams.com\/blog\/wp-json\/wp\/v2\/tags?post=232420"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}