mystic 0.4.2-1 source package in Ubuntu
Changelog
mystic (0.4.2-1) unstable; urgency=medium * Team upload. * New upstream release. * debian/watch: Fix monitoring script. * debian/control: Update Built-Using field for doc package. -- Boyuan Yang <email address hidden> Thu, 01 Feb 2024 19:12:43 -0500
Upload details
- Uploaded by:
- Debian Python Team
- Uploaded to:
- Sid
- Original maintainer:
- Debian Python Team
- Architectures:
- all
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section | |
|---|---|---|---|---|
| Oracular | release | universe | misc | |
| Noble | release | universe | misc |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| mystic_0.4.2-1.dsc | 3.7 KiB | fe015c985051d65883d42a5921c621ca75c118065195982faf54c1624e2fc5a5 |
| mystic_0.4.2.orig.tar.gz | 795.2 KiB | 9423f313e114b089162fd11eeef5ce075c502ce012a65caad12948fd631e7eb6 |
| mystic_0.4.2-1.debian.tar.xz | 4.1 KiB | ec7eae62f4a7b18874456dc9bc3549e5711ea7373ccf1cbf8b19b42efab64437 |
Available diffs
- diff from 0.4.1-1 to 0.4.2-1 (34.0 KiB)
No changes file available.
Binary packages built by this source
- python-mystic-doc: Constrained nonlinear optimization (documentation)
The mystic framework provides a collection of optimization algorithms
and tools that allows the user to more robustly (and easily) solve
hard optimization problems for machine learning, uncertainty
quantification and AI. mystic gives the user fine-grained power to
both monitor and steer optimizations as the fit processes are
running. Users can customize optimizer stop conditions, where both
compound and user-provided conditions may be used. Optimizers can
save state, can be reconfigured dynamically, and can be restarted
from a saved solver or from a results file. All solvers can also
leverage parallel computing, either within each iteration or as an
ensemble of solvers.
.
mystic provides a stock set of configurable, controllable solvers
with:
* a common interface
* a control handler with: pause, continue, exit, and callback
* ease in selecting initial population conditions: guess, random, etc
* ease in checkpointing and restarting from a log or saved state
* the ability to leverage parallel & distributed computing
* the ability to apply a selection of logging and/or verbose monitors
* the ability to configure solver-independent termination conditions
* the ability to impose custom and user-defined penalties and constraints
.
mystic is part of pathos, a Python framework for heterogeneous computing.
.
This package contains the mystic documentation in HTML format.
- python3-mystic: Constrained nonlinear optimization
The mystic framework provides a collection of optimization algorithms
and tools that allows the user to more robustly (and easily) solve
hard optimization problems for machine learning, uncertainty
quantification and AI. mystic gives the user fine-grained power to
both monitor and steer optimizations as the fit processes are
running. Users can customize optimizer stop conditions, where both
compound and user-provided conditions may be used. Optimizers can
save state, can be reconfigured dynamically, and can be restarted
from a saved solver or from a results file. All solvers can also
leverage parallel computing, either within each iteration or as an
ensemble of solvers.
.
mystic provides a stock set of configurable, controllable solvers
with:
* a common interface
* a control handler with: pause, continue, exit, and callback
* ease in selecting initial population conditions: guess, random, etc
* ease in checkpointing and restarting from a log or saved state
* the ability to leverage parallel & distributed computing
* the ability to apply a selection of logging and/or verbose monitors
* the ability to configure solver-independent termination conditions
* the ability to impose custom and user-defined penalties and constraints
.
mystic is part of pathos, a Python framework for heterogeneous computing.
