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In his article, [1] Milne-Thomson considers the problem of finding () when 1. u ( x , y ) {\displaystyle u(x,y)} and v ( x , y ) {\displaystyle v(x,y)} are given, 2. u ( x , y ) {\displaystyle u(x,y)} is given and f ( z ) {\displaystyle f(z)} is real on the real axis, 3. only u ( x , y ) {\displaystyle u(x,y)} is given, 4. only v ( x , y ...
The solution = is in fact a valid solution to the original equation; but the other solution, =, has disappeared. The problem is that we divided both sides by x {\displaystyle x} , which involves the indeterminate operation of dividing by zero when x = 0. {\displaystyle x=0.}
Note that this second solution is not related to the first through a coordinate transformation, but it is a solution nevertheless. Here is the problem that disturbed Einstein so much: if these coordinates systems differ only after = there are then two solutions; they have the same initial conditions but they impose different geometries after =.
For example, if 2 pigeons are randomly assigned to 4 pigeonholes, there is a 25% chance that at least one pigeonhole will hold more than one pigeon; for 5 pigeons and 10 holes, that probability is 69.76%; and for 10 pigeons and 20 holes it is about 93.45%.
Technically, a point z 0 is a pole of a function f if it is a zero of the function 1/f and 1/f is holomorphic (i.e. complex differentiable) in some neighbourhood of z 0. A function f is meromorphic in an open set U if for every point z of U there is a neighborhood of z in which at least one of f and 1/f is holomorphic.
Noteworthy examples of vacuum solutions, electrovacuum solutions, and so forth, are listed in specialized articles (see below). These solutions contain at most one contribution to the energy–momentum tensor, due to a specific kind of matter or field. However, there are some notable exact solutions which contain two or three contributions ...
For some functions, Steffensen's method can work even if this condition is not met, but in such a case, the starting value must be very close to the actual solution , and convergence to the solution may be slow. Adjustment of the size of the method's intermediate step, mentioned later, can improve convergence in some of these cases.
In this method, random simulations are used to find an approximate solution. Example: The traveling salesman problem is what is called a conventional optimization problem. That is, all the facts (distances between each destination point) needed to determine the optimal path to follow are known with certainty and the goal is to run through the ...