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The conjugate gradient method can be applied to an arbitrary n-by-m matrix by applying it to normal equations A T A and right-hand side vector A T b, since A T A is a symmetric positive-semidefinite matrix for any A. The result is conjugate gradient on the normal equations (CGN or CGNR). A T Ax = A T b
Consider minimizing the function () = ‖ ~ ~ ‖.Since this is a convex function, a sufficient condition for optimality is that the gradient is zero (() =) which gives rise to the equation
A sufficient (but not necessary) condition for the method to converge is that the matrix A is strictly or irreducibly diagonally dominant. Strict row diagonal dominance means that for each row, the absolute value of the diagonal term is greater than the sum of absolute values of other terms:
So now we consider the problem’s given initial conditions (the problem including given initial conditions is the so-called initial value problem). Suppose we are given x ( 0 ) = y ( 0 ) = 1 {\displaystyle x(0)=y(0)=1} , which plays the role of starting point for our ordinary differential equation; application of these conditions specifies the ...
The TI-84 Plus C Silver Edition was released in 2013 as the first Z80-based Texas Instruments graphing calculator with a color screen.It had a 320×240-pixel full-color screen, a modified version of the TI-84 Plus's 2.55MP operating system, a removable 1200 mAh rechargeable lithium-ion battery, and keystroke compatibility with existing math and programming tools. [6]
In matrix calculus, Jacobi's formula expresses the derivative of the determinant of a matrix A in terms of the adjugate of A and the derivative of A. [1]If A is a differentiable map from the real numbers to n × n matrices, then
They are evaluated to return a value when used in an expression or program. Specific values, (constant, C) can be plugged in for the independent variable (X) by following the equation name (dependent, Y) by the constant value in parentheses. In the example below, "(4)" is used (for no particular reason). (Y1(4) would return the value of Y1 at X=4)
A Sylvester equation has a unique solution for X exactly when there are no common eigenvalues of A and −B. More generally, the equation AX + XB = C has been considered as an equation of bounded operators on a (possibly infinite-dimensional) Banach space. In this case, the condition for the uniqueness of a solution X is almost the same: There ...