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Byju's (stylised as BYJU'S) is an Indian multinational educational technology company, headquartered in Bengaluru. [4] It was founded in 2011 by Byju Raveendran and Divya Gokulnath . As of October 2024, various media outlets reported that Byju's valuation has now plummeted to zero, down from its peak valuation of $22 billion in 2022.
Lagrange's four-square theorem, also known as Bachet's conjecture, states that every nonnegative integer can be represented as a sum of four non-negative integer squares. [1] That is, the squares form an additive basis of order four. = + + + where the four numbers ,,, are integers
The term Fibonacci sequence is also applied more generally to any function from the integers to a field for which (+) = + (+).These functions are precisely those of the form () = () + (), so the Fibonacci sequences form a vector space with the functions () and () as a basis.
Negative numbers are used to describe values on a scale that goes below zero, such as the Celsius and Fahrenheit scales for temperature. The laws of arithmetic for negative numbers ensure that the common-sense idea of an opposite is reflected in arithmetic. For example, − (−3) = 3 because the opposite of an opposite is the original value.
Byju’s distress has weighed on India’s edtech industry in particular. The sector received $712 million in funding in 2023, down from $2.9 billion in 2022 and $5.3 billion in 2021.
On January 25, 2024, lenders began bankruptcy proceedings against GeoGebra's parent company Byju's in an effort to repay its loans. [8] On February 1, 2024, Byju's U.S. division filed for Chapter 11 bankruptcy in Delaware. Byju's would raise around $200 million in an effort to clear "immediate liabilities" and for other operational costs. [9]
Insolvency proceedings against ed-tech giant Byju's, once India's biggest startup valued at $22 billion, will likely force thousands of employees to quit and result in a total shutdown of its ...
In mathematical optimization, the method of Lagrange multipliers is a strategy for finding the local maxima and minima of a function subject to equation constraints (i.e., subject to the condition that one or more equations have to be satisfied exactly by the chosen values of the variables). [1]