conj — Compute complex conjugates element-wise in MATLAB and RunMat.

conj(x) negates the imaginary component of each element in x, while real values remain unchanged. The operation is element-wise for scalars and arrays, matching MATLAB behavior.

Syntax

Y = conj(X)

Inputs

Returns

Errors

How conj works

• Complex scalars and arrays have their imaginary components multiplied by -1; when a result has no imaginary part it collapses to a real scalar or tensor just like MATLAB.
• Purely real numeric inputs (double, single, integer) are returned unchanged.
• Logical arrays are promoted to double precision with true → 1.0 and false → 0.0.
• Character arrays are promoted to double precision containing their Unicode code points; the Conjugate operation does not alter the codes because they are real-valued.
• String arrays are not supported and raise an error.

Does RunMat run conj on the GPU?

Hook available: Real tensors stay on the GPU and are processed in-place (both the in-process provider used for tests and the WGPU provider expose this path).

Fusion and auto-offload: Because conj is tagged as an elementwise unary builtin, the fusion planner treats it as a pass-through for real-valued kernels. Native auto-offload therefore keeps fused expressions resident on the GPU whenever the surrounding ops are profitable.

Hook missing or complex input: RunMat gathers the data to host memory, applies the CPU semantics (including complex negation and type promotion), and returns the result. The planner may re-upload tensors later if profitable.

Complex tensors are currently materialised on the host because GPU-side complex layouts are still under development. Providers can add native complex kernels later without changing this builtin.

GPU memory and residency

You usually do not need to call gpuArray explicitly. RunMat's fusion planner and Accelerate layer manage residency and offload decisions automatically, keeping tensors on the GPU whenever device execution is beneficial. Explicit gpuArray and gather remain available for MATLAB compatibility or fine-grained residency control.

Examples

Complex conjugate of a scalar value in MATLAB

z = 3 + 4i;
result = conj(z)

Expected output:

result = 3 - 4i

Apply conj to every element of a complex matrix

Z = [1+2i, 4-3i; -5+0i, 7+8i];
C = conj(Z)

Expected output:

C =
   1 - 2i   4 + 3i
  -5 + 0i   7 - 8i

Ensure conj leaves real inputs unchanged

data = [-2.5 0 9.75];
unchanged = conj(data)

Expected output:

unchanged = [-2.5 0 9.75]

Use conj on logical masks converted to doubles

mask = logical([0 1 0; 1 1 0]);
numeric = conj(mask)

Expected output:

numeric =
     0     1     0
     1     1     0

Convert MATLAB char arrays to numeric codes with conj

chars = 'RunMat';
codes = conj(chars)
outputClass = class(codes)

Expected output:

codes = [82 117 110 77 97 116];
outputClass = 'double'

Compute conjugate on GPU-resident arrays

G = rand(4096, 256, "gpuArray");
H = conj(G)

Using conj with coding agents

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

FAQ

Related Math functions

Open-source implementation

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

• View the source for conj 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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