Concept

Matrix exponential

The power series 1 + A + A²/2 + A³/6 + …, applied to a square matrix A, which always converges. It solves the linear differential equation with matrix A, and unlike the logarithm it can be applied to every square matrix.

Named by 5 essays across one field — each of them below, with the objects they name alongside it.

The flow of a linear equation, and the matrix that runs it for one unit of time. Paths of points moving so that their velocity is [0.25, −1.2, 1.2, 0.25] applied to their position, with the position after time 1 marked on each; the matrix taking start to finish is e^A.

The exponential of a square

The series for e makes perfect sense with a matrix in it. What comes out solves a system of equations the way the ordinary exponential solves one, and a skew matrix exponentiates into a rotation with no trigonometry anywhere.

analysis · The exponential
The trace–determinant plane and the flows it sorts. The plane of trace against determinant, divided by the horizontal axis and the parabola tr² = 4 det into saddle, node, spiral and centre regions, with 6 matrices marked and their phase portraits drawn alongside.

Two numbers decide the flow

A linear system in the plane has four coefficients, and what its solutions do forever afterwards — spiral in, race out, swing round, or split along two lines — is decided by two of the numbers made from them. The plane of trace against determinant is a complete map of the possibilities, and the only places it cannot decide are the lines where it changes its mind.

analysis · The exponential
Which real 2 × 2 matrices have a real logarithm, read off their eigenvalues. Eigenvalues of 7 matrices plotted in the complex plane with the negative real axis emphasised, beside a table saying for each whether a real logarithm exists and why: A yes, B yes, C yes, D no, E no, F yes, G no.

The matrix that has no logarithm

Every square matrix has an exponential, and a matrix exponential is always invertible. The converse fails, and it fails in a way that can be read straight off the eigenvalues — a real matrix with eigenvalues −1 and −2 is no exponential at all, while minus the identity is the exponential of a whole continuum of matrices that do not even commute with each other.

analysis · The exponential
How many three-state chains constant rates can produce. s 0.05: 94.9%; s 0.1: 90.2%; s 0.2: 79.6%; s 0.3: 70.3%; s 0.5: 47.7%; s 0.7: 22.9%; s 1: 4.3%.

When a table of moves came from steady rates

A process that jumps between states at constant rates, watched once a year, produces a table of yearly moves that is the exponential of its rates. Most tables that anyone could write down are not — a random three-state table is only about one time in twenty-three — and the few that are can have two different sets of rates behind them, but only once the process has forgotten where it started.

analysis · The exponential
Fewer tables have rates behind them as states are added. n=2: 0.02:1.000 0.05:1.000 0.1:1.000 0.2:1.000 0.3:1.000 0.5:1.000 0.7:0.847 1:0.489; n=3: 0.02:0.975 0.05:0.945 0.1:0.881 0.2:0.797 0.3:0.690 0.5:0.453 0.7:0.244 1:0.043; n=4: 0.02:0.941 0.05:0.865 0.1:0.735 0.2:0.520 0.3:0.329 0.5:0.108 0.7:0.019 1:0.001; n=5: 0.02:0.896 0.05:0.755 0.1:0.526 0.2:0.275 0.3:0.130 0.5:0.020 0.7:0.001 1:0.000; n=6: 0.02:0.807 0.05:0.575 0.1:0.388 0.2:0.090 0.3:0.025 0.5:0.000 0.7:0.000 1:0.000

The route round that rates cannot avoid

A table of yearly moves between states came from steady rates only if it is the exponential of a rate matrix. With two states that is true of half of all tables, with three of one in twenty-four, with four of about one in two thousand, and of none of 400,000 tables drawn at random with five. Even a table that is nine parts staying put fails more and more often as states are added, and always for one reason: rates that carry 1 to 2 and 2 to 3 also carry 1 to 3, and a table whose direct move is rarer than that route round cannot have come from them.

analysis · The exponential

Named alongside it

The objects these essays reach for when they reach for this one.

DeterminantEigenvalueLogarithmTraceDifferential equationMarkov chainPower seriesRotationTransition matrixe, the numberEigenvectorMatrix

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