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Title | Welcome to CVXPY 1.3 — CVXPY 1.3 |
Description | An open source Python-embedded modeling language for convex optimization problems. Express your problem in a natural way that follows the |
Keywords | convex optimization, open source, software |
WebSite | cvxpy.org |
Host IP | 162.255.119.149 |
Location | United States |
Site | Rank |
US$15,771,480
Last updated: 2023-05-07 23:34:28
cvxpy.org has Semrush global rank of 671,104. cvxpy.org has an estimated worth of US$ 15,771,480, based on its estimated Ads revenue. cvxpy.org receives approximately 1,819,787 unique visitors each day. Its web server is located in United States, with IP address 162.255.119.149. According to SiteAdvisor, cvxpy.org is safe to visit. |
Purchase/Sale Value | US$15,771,480 |
Daily Ads Revenue | US$14,559 |
Monthly Ads Revenue | US$436,749 |
Yearly Ads Revenue | US$5,240,984 |
Daily Unique Visitors | 121,320 |
Note: All traffic and earnings values are estimates. |
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cvxpy.org. | A | 1799 | IP: 185.199.108.153 |
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Welcome to CVXPY 1.3 ¶ Convex optimization, for everyone. We are building a CVXPY community on Discord . Join the conversation! CVXPY is an open source Python-embedded modeling language for convex optimization problems. It lets you express your problem in a natural way that follows the math, rather than in the restrictive standard form required by solvers. For example, the following code solves a least-squares problem with box constraints: import cvxpy as cp import numpy as np # Problem data. m = 30 n = 20 np . random . seed ( 1 ) A = np . random . randn ( m , n ) b = np . random . randn ( m ) # Construct the problem. x = cp . Variable ( n ) objective = cp . Minimize ( cp . sum_squares ( A @ x - b )) constraints = [ 0 <= x , x <= 1 ] prob = cp . Problem ( objective , constraints ) # The optimal objective value is returned by `prob.solve()`. result = prob . solve () # The optimal value for x is stored in `x.value`. print ( x . value ) # The optimal Lagrange multiplier for a |
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