Using argmin

In order to use argmin, one needs to add both argmin and argmin-math to Cargo.toml:

argmin = { version = "0.6.0" }
argmin-math = { version = "0.1.0", features = ["ndarray_latest-serde,nalgebra_latest-serde"] }

or, for the current development version:

argmin = { git = "" }
argmin-math = { git = "", features = ["ndarray_latest-serde,nalgebra_latest-serde"] }

Via adding argmin-math one can choose which math backend should be available (and whether serde-support is enabled or not). The examples above use both the ndarray_latest-serde and nalgebra_latest-serde features of argmin-math. For details on which options are available in argmin-math, please refer to the documentation.

Crate features

argmin offers a number of features which can be enabled or disabled depending on your needs.


  • slog-logger: Support for logging observers based on slog.
  • serde1: Support for serde. Needed for checkpointing and writing parameters to disk as well as logging to disk. Deactivating this feature leads to fewer dependencies and can lower compilation time, but it will also disable checkpointing and logging to disk.


  • ctrl: This feature uses the ctrlc crate to properly stop the optimization (and return the current best result) after pressing Ctrl+C during an optimization run.
  • rayon: This feature adds rayon as a depenceny and allows for parallel computation of cost functions, operators, gradients, Jacobians and Hessians. Note that only solvers that operate on multiple parameter vectors per iteration benefit from this feature (e.g. Particle Swarm Optimization).

Experimental support for compiling to WebAssembly

Compiling to WASM requires the feature wasm-bindgen. WASM support is still experimental. Please report any issues you encounter when using argmin in a WASM context.

Which math backend to use

argmin offers abstractions over basic Vecs, ndarray and nalgebra types. For performance reasons, the latter two should be prefered. Which one to use is a matter of taste and may depend on what you are already using.

Vecs on the other hand do not have very efficient implementations for the different mathematical operations and therefore are not well suited for solvers which heavily rely on matrix operations. However, Vecs are suitable for solvers such as Simulated Annealing and Particle Swarm Optimization, which mainly operate on the parameter vectors themselves.