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Mahāvīra (or Mahaviracharya, "Mahavira the Teacher") was a 9th-century Indian Jain mathematician possibly born in Mysore, in India. [1] [2] [3] He authored Gaṇita-sāra-saṅgraha (Ganita Sara Sangraha) or the Compendium on the gist of Mathematics in 850 CE. [4] He was patronised by the Rashtrakuta emperor Amoghavarsha. [4]
Āryabhaṭa did not give the algorithm the name Kuṭṭaka, and his description of the method was mostly obscure and incomprehensible. It was Bhāskara I (c. 600 – c. 680) who gave a detailed description of the algorithm with several examples from astronomy in his Āryabhatiyabhāṣya , who gave the algorithm the name Kuṭṭaka .
The field of numerical analysis predates the invention of modern computers by many centuries. Linear interpolation was already in use more than 2000 years ago. Many great mathematicians of the past were preoccupied by numerical analysis, [5] as is obvious from the names of important algorithms like Newton's method, Lagrange interpolation polynomial, Gaussian elimination, or Euler's method.
Rangachar Narayana Iyengar (born 2 June 1943), also known as RNI, is a civil engineer and professor from India. He was with the Indian Institute of Science , Bangalore for about four decades. He has been the director of Central Building Research Institute, Roorkee (1994–2000).
Necessary conditions for a numerical method to effectively approximate (,) = are that and that behaves like when . So, a numerical method is called consistent if and only if the sequence of functions { F n } n ∈ N {\displaystyle \left\{F_{n}\right\}_{n\in \mathbb {N} }} pointwise converges to F {\displaystyle F} on the set S {\displaystyle S ...
In numerical analysis and applied mathematics, sinc numerical methods are numerical techniques [1] for finding approximate solutions of partial differential equations and integral equations based on the translates of sinc function and Cardinal function C(f,h) which is an expansion of f defined by
Method of successive substitution (number theory) Monte Carlo method (computational physics, simulation) Newton's method (numerical analysis) Pemdas method (order of operation) Perturbation methods (functional analysis, quantum theory) Probabilistic method (combinatorics) Romberg's method (numerical analysis) Runge–Kutta method (numerical ...
The Numerical Recipes books cover a range of topics that include both classical numerical analysis (interpolation, integration, linear algebra, differential equations, and so on), signal processing (Fourier methods, filtering), statistical treatment of data, and a few topics in machine learning (hidden Markov model, support vector machines).