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Title | JuliaDiff |
Description | JuliaDiff Differentiation tools in Julia . JuliaDiff on GitHub Stop approximating derivatives! Derivatives are required at the core of many numerical algo |
Keywords | N/A |
WebSite | juliadiff.org |
Host IP | 185.199.109.153 |
Location | - |
Site | Rank |
US$1,092,678
Last updated: 2023-05-10 15:59:26
juliadiff.org has Semrush global rank of 9,686,571. juliadiff.org has an estimated worth of US$ 1,092,678, based on its estimated Ads revenue. juliadiff.org receives approximately 126,079 unique visitors each day. Its web server is located in -, with IP address 185.199.109.153. According to SiteAdvisor, juliadiff.org is safe to visit. |
Purchase/Sale Value | US$1,092,678 |
Daily Ads Revenue | US$1,009 |
Monthly Ads Revenue | US$30,259 |
Yearly Ads Revenue | US$363,106 |
Daily Unique Visitors | 8,406 |
Note: All traffic and earnings values are estimates. |
Host | Type | TTL | Data |
juliadiff.org. | A | 1798 | IP: 185.199.109.153 |
juliadiff.org. | A | 1798 | IP: 185.199.111.153 |
juliadiff.org. | A | 1798 | IP: 185.199.108.153 |
juliadiff.org. | A | 1798 | IP: 185.199.110.153 |
juliadiff.org. | NS | 1800 | NS Record: dns1.registrar-servers.com. |
juliadiff.org. | NS | 1800 | NS Record: dns2.registrar-servers.com. |
JuliaDiff Differentiation tools in Julia . JuliaDiff on GitHub Stop approximating derivatives! Derivatives are required at the core of many numerical algorithms. Unfortunately, they are usually computed inefficiently and approximately by some variant of the finite difference approach f ′ ( x ) ≈ f ( x + h ) − f ( x ) h , h small . f’(x) \approx \frac{f(x+h) - f(x)}{h}, h \text{ small }. f ′ ( x ) ≈ h f ( x + h ) − f ( x ) , h small . This method is inefficient because it requires Ω ( n ) \Omega(n) Ω ( n ) evaluations of f : R n → R f : \mathbb{R}^n \to \mathbb{R} f : R n → R to compute the gradient ∇ f ( x ) = ( ∂ f ∂ x 1 ( x ) , ⋯ , ∂ f ∂ x n ( x ) ) \nabla f(x) = \left( \frac{\partial f}{\partial x_1}(x), \cdots, \frac{\partial f}{\partial x_n}(x)\right) ∇ f ( x ) = ( ∂ x 1 ∂ f ( x ) , ⋯ , ∂ x n ∂ f ( x ) ) , for example. It is approximate because we have to choose some finite, small value of the step length h h h , balancing floating-point precision with mathematical |
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