Package: mize 0.2.5.9002

mize: Unconstrained Numerical Optimization Algorithms

Optimization algorithms implemented in R, including conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited memory BFGS (L-BFGS) methods. Most internal parameters can be set through the call interface. The solvers hold up quite well for higher-dimensional problems.

Authors:James Melville [aut, cre]

mize_0.2.5.9002.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mize/json (API)

# Install 'mize' in R:
install.packages('mize', repos = c('https://jlmelville.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jlmelville/mize/issues

Pkgdown/docs site:https://jlmelville.github.io

On CRAN:

Conda:

conjugate-gradientl-bfgsnumerical-optimization

8.18 score 12 stars 4 packages 35 scripts 483 downloads 7 exports 0 dependencies

Last updated from:5ec90ec3db. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK165
source / vignettesOK211
linux-releaseOK157
macos-releaseOK88
macos-oldrelOK136
windows-develOK119
windows-releaseOK92
windows-oldrelOK97
wasm-releaseOK109

Exports:check_mize_convergencecheck_mize_gradientmake_mizemizemize_initmize_stepmize_step_summary

Dependencies:

Choosing methods and tuning
Start here | Before changing the method | Change the direction method when | Tune the line search when | Wolfe conditions and c2 | Initial step estimates | Backtracking, constant, and bold-driver steps | Momentum and adaptive learning-rate methods | Nesterov Accelerated Gradient ("NAG") | Momentum schedules and update order | Delta-Bar-Delta learning rates | Adaptive restart | See also
Rendered frommethods.Rmdusingknitr::rmarkdown

Last update: 2026-08-25
Started: 2026-08-24

Convergence
Choose stopping controls | Iteration limit | Function-change tolerances | Gradient tolerances | Step tolerance | Evaluation budgets | When several criteria are active | Failure result | Checking tolerances less often | Best and last results | See also
Rendered fromconvergence.Rmdusingknitr::rmarkdown

Last update: 2026-08-25
Started: 2017-01-08

Getting started
Define the objective and gradient | Choose a starting point and check the gradient | Run the optimization | Read the termination record | Inspect progress | Where to go next
Rendered frommize.Rmdusingknitr::rmarkdown

Last update: 2026-08-25
Started: 2016-12-29

Stateful optimization
Why retaining state matters | The stateful lifecycle | Optional observations | A minimal terminal-aware loop | Work safely between steps | Deliberate observations and diagnostics | Hard budgets can deny a requested observation | Checkpoint and resume | Reinitialization starts a new run | See also
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Last update: 2026-08-25
Started: 2016-12-27

Metric MDS
Stress and its gradient | Validate the gradient | Optimize with separate and combined callbacks | Put the configuration on the map | See also
Rendered frommmds.Rmdusingknitr::rmarkdown

Last update: 2026-08-24
Started: 2016-12-27