diag — Create diagonal matrices or extract diagonals in MATLAB and RunMat.

diag either constructs a diagonal matrix from a vector or extracts a diagonal from a matrix. Offset handling and supported input classes follow MATLAB semantics.

Syntax

B = diag(A)
B = diag(A, k)
B = diag(A, sz)
B = diag(A, k, sz)
B = diag(A, "vector")
B = diag(A, class)
B = diag(A, "like", prototype)
B = diag(A, args...)

Inputs

Returns

Errors

How diag works

• diag(v) with a vector v returns a square matrix whose main diagonal is v.
• diag(v, k) places v on the k-th diagonal: super-diagonals for k > 0, sub-diagonals for k < 0. The output size grows by abs(k).
• diag(A) with a matrix A returns a column vector containing the main diagonal of A.
• diag(A, k) extracts the k-th diagonal. When the requested diagonal does not exist, an empty column vector is returned.
• diag(v, 'vector') always returns a column vector copy of v, even when v is already a vector.
• diag(v, [m n]) creates an explicit rectangular size. You can combine it with offsets (e.g. diag(v, k, [m n])) when you need a wider diagonal band.
• diag(___, 'logical') converts the result to a logical array. diag(___, 'double') forces a double-precision result when inputs are logical.
• diag(___, 'like', prototype) matches the numeric flavour and residency of prototype (including GPU handles).
• Logical inputs stay logical; complex inputs stay complex; character arrays preserve padding with spaces off the diagonal.
• Higher-dimensional inputs are accepted when trailing dimensions are singleton—only the leading 2-D slice participates in the diagonal operation.

Does RunMat run diag on the GPU?

When the input lives on the GPU, RunMat calls the acceleration provider's diag_from_vector or diag_extract hook (see the GPU spec). Providers that do not expose these hooks fall back to a host round-trip: the input is gathered once, the diagonal computation runs on the CPU, and the result is uploaded back to the device. Size overrides or the 'vector' option also trigger the host path because they are not yet exposed through provider hooks. 'like' requests are honoured regardless of the path: GPU prototypes stay on the device, while logical or complex prototypes adjust the element type accordingly.

GPU memory and residency

You usually do NOT need to call gpuArray yourself in RunMat (unlike MATLAB).

In RunMat, the auto-offload planner keeps residency on the GPU when expressions make use of GPU providers. Even when the provider lacks diag_from_vector / diag_extract, the builtin gathers once on the host, performs the diagonal operation, and re-uploads the result so later GPU-friendly ops can continue without intervention.

To preserve backwards compatibility with MathWorks MATLAB, and for when you want to explicitly bootstrap GPU residency, you can call gpuArray to move data to the GPU. That mirrors MATLAB's behaviour while still allowing RunMat's planner to decide whether the GPU offers an advantage for the surrounding computation.

Since MathWorks MATLAB does not have a fusion planner, and they kept their parallel execution toolbox separate from the core language, as their toolbox is a separate commercial product, MathWorks MATLAB users need to call gpuArray to move data to the GPU manually whereas RunMat users can rely on the fusion planner to keep data on the GPU automatically.

Examples

Creating a diagonal matrix from a vector

v = [4 5 6];
D = diag(v)

Expected output:

D =
     4     0     0
     0     5     0
     0     0     6

Placing a vector on an upper diagonal

v = [1 2 3];
U = diag(v, 1)

Expected output:

U =
     0     1     0     0
     0     0     2     0
     0     0     0     3
     0     0     0     0

Extracting a subdiagonal as a column vector

A = [1 2 3; 4 5 6; 7 8 9];
d = diag(A, -1)

Expected output:

d =
     4
     8

Building a diagonal matrix from a logical mask

mask = logical([1 0 1 0]);
M = diag(mask)

Expected output:

M =
     1     0     0     0
     0     0     0     0
     0     0     1     0
     0     0     0     0

Keeping diagonal results on the GPU

G = gpuArray([2; 4; 8]);
D = diag(G);
firstTwo = gather(D(1:2, 1:2))

Expected output:

firstTwo =
     2     0
     0     4

Returning a vector without creating a matrix

v = [10 20 30];
d = diag(v, 'vector')

Expected output:

d =
    10
    20
    30

Creating a rectangular diagonal matrix with sz

v = [1 2];
R = diag(v, [2 4])

Expected output:

R =
     1     0     0     0
     0     2     0     0

Matching residency and type with 'like'

G = gpuArray([1 3 5]');
D = diag([1 2 3], 'like', G)

Using diag with coding agents

Open a RunMat example with live inputs, then ask the agent to explain how diag changes the result.

FAQ

Related Array functions

Open-source implementation

Unlike proprietary runtimes, every RunMat function is open-source. Read exactly how diag is executed, line by line, in Rust.

• View the source for diag in Rust on GitHub
• Learn how the RunMat runtime works
• Found a bug? Open an issue with a minimal reproduction.

About RunMat

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