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SQL-92 was the third revision of the SQL database query language. Unlike SQL-89, it was a major revision of the standard. Aside from a few minor incompatibilities, the SQL-89 standard is forward-compatible with SQL-92. The standard specification itself grew about five times compared to SQL-89.
A transposition table is a cache whose maximum size is limited by available system memory, and it may overflow at any time. In fact, it is expected to overflow, and the number of positions cacheable at any time may be only a small fraction (even orders of magnitude smaller) than the number of nodes in the game tree.
In linear algebra, the transpose of a matrix is an operator which flips a matrix over its diagonal; that is, it switches the row and column indices of the matrix A by producing another matrix, often denoted by A T (among other notations). [1] The transpose of a matrix was introduced in 1858 by the British mathematician Arthur Cayley. [2]
SQL/PSM (SQL/Persistent Stored Modules) is an ISO standard mainly defining an extension of SQL with a procedural language for use in stored procedures.Initially published in 1996 as an extension of SQL-92 (ISO/IEC 9075-4:1996, a version sometimes called PSM-96 or even SQL-92/PSM [2]), SQL/PSM was later incorporated into the multi-part SQL:1999 standard, and has been part 4 of that standard ...
Given a function that accepts an array, a range query (,) on an array = [,..,] takes two indices and and returns the result of when applied to the subarray [, …,].For example, for a function that returns the sum of all values in an array, the range query (,) returns the sum of all values in the range [,].
SQL:2011 or ISO/IEC 9075:2011 (under the general title "Information technology – Database languages – SQL") is the seventh revision of the ISO (1987) and ANSI (1986) standard for the SQL database query language. It was formally adopted in December 2011. [1] The standard consists of 9 parts which are described in detail in SQL.
For example, for the 2×2 matrix = [], the vectorization is = []. The connection between the vectorization of A and the vectorization of its transpose is given by the commutation matrix . Compatibility with Kronecker products
(This is just a consequence of the fact that the inverse of an N×M transpose is an M×N transpose, although it is also easy to show explicitly that P −1 composed with P gives the identity.) As proved by Cate & Twigg (1977), the number of fixed points (cycles of length 1) of the permutation is precisely 1 + gcd( N −1, M −1) , where gcd is ...