Bessa Research Group

L2CO Tasks¤

Optimization tasks compatible with the L2CO library

First publication: April 13, 2026

Summary¤

l2co-tasks provides a unified collection of optimization tasks for the L2CO ecosystem. Each Task bundles a JAX/Equinox model, a loss function, an optional dataset, and metadata tags into a single, serializable object that any L2CO optimizer or rollout can consume. The package ships ready-made task families — analytic black-box benchmarks (BBOB and CEC 2005), quadratic problems, supervised-learning tasks (spiral, MNIST-1D, Gaussian classification), and PINN-style PDE problems (convection, reaction, wave) — together with create_*_task factories and f3dasm samplers for building experiment datasets.

Statement of need¤

Research on learning to optimize and optimizer selection requires evaluating many optimizers across a diverse, well-characterized set of problems — but these problems usually come from incompatible sources with different input ranges, calling conventions, and metadata. l2co-tasks standardizes them behind a single Task abstraction: inputs are normalized to [0, 1]^d and scaled inside the loss, stochasticity and known global minima are tracked explicitly, and every task carries a hashable tag and serializes to a single .eqx file. This makes tasks reproducible, portable, and directly pluggable into the l2co / rl2co rollout machinery and f3dasm experiment pipelines.

Authorship¤

Authors: - Martin van der Schelling (m.p.vanderschelling@tudelft.nl)

Authors affiliation: - Delft University of Technology (Bessa Research Group)

Maintainer: - Martin van der Schelling (m.p.vanderschelling@tudelft.nl)

Maintainer affiliation: - Delft University of Technology (Bessa Research Group)

Getting started¤

l2co-tasks is uv-managed and depends on an editable install of a sibling f3dasm checkout, so lay the repositories out side-by-side before syncing:

git clone https://github.com/bessagroup/f3dasm.git
git clone https://github.com/bessagroup/l2co-tasks.git
cd l2co-tasks
uv sync

Create a task from one of the factories and evaluate its loss:

from l2co_tasks import create_bbob_task

task = create_bbob_task(fn_name="sphere", seed=0, dimensionality=2)
loss = task.loss_fn(task.model)   # model is the [0, 1]^d input vector

Tasks serialize to a single-file .eqx format via Task.save / Task.load. See the API reference for the full list of task families and create_*_task factories. To build your own task from scratch, see the Create your own task guide.

Available tasks¤

Every task is built by a create_<name>_task(...) factory and returned as a single Task object. The package ships the following families:

Category	Task	Factory	Description	Reference
Black-box	BBOB	`create_bbob_task`	24 analytic, noiseless black-box functions over `[0, 1]^d`; optional multiplicative Gaussian noise.	bbob-jax
Black-box	BBOB-noisy	`create_bbob_noisy_task`	30 inherently stochastic functions (`bbob_noisy_f101` ... `bbob_noisy_f130`): Gaussian/uniform/Cauchy noise at moderate or severe severity; needs bbob-jax > 1.8.0.	bbob-jax
Black-box	CEC 2005	`create_cec2005_task`	CEC 2005 real-parameter functions with per-function bounds; `f4/f17/f24/f25` are stochastic.	bbob-jax
Black-box	CEC 2017	`create_cec2017_task`	CEC 2017 bound-constrained functions (`cec2017_f1`, `cec2017_f3` ... `cec2017_f30`); hybrids need a minimum dimensionality (`tag["min_ndim"]`).	bbob-jax
Black-box	Embedded BBOB	`create_embedded_bbob_task`	BBOB function hidden in a higher-dimensional space via a random orthonormal embedding; rank-`d` Hessian outliers plus a flat or `bulk_scale`-curved null space, mimicking neural-network loss landscapes.	Li et al. (2018), Wang et al. (2016)
Least-squares	Random quadratic	`create_quadratic_task`	Minimize `\\|\\|W x - y\\|\\|^2` for random Gaussian `W`, `y` (square or over-determined).	Maheswaranathan et al. (2019)
Supervised	Two-spiral	`create_spiral_task`	GRU-RNN trained with MSE to separate two interleaved spirals.	—
Supervised	MNIST-1D	`create_mnist1d_task`	MLP softmax classifier on the 1-D MNIST surrogate dataset.	Greydanus (2020)
Supervised	Gaussian blobs	`create_gaussian_task`	MLP classifier (cross-entropy + L2) on Gaussian-cluster data.	—
Meta-learning	Adam hyperparameters	`create_gaussian_meta_task`	Outer objective tunes Adam's `(lr, b1, b2)` for an inner MLP training run.	—
PINN	1-D PDE	`create_pde_task`	MLP PINN for the `convection`, `reaction`, or `wave` equation (collocation-residual MSE).	—
PINN	Helmholtz	`create_helmholtz_task`	Fourier-feature MLP for the 2-D/3-D Helmholtz equation.	Jnini et al. (2026)
PINN	Stokes	`create_stokes_task`	MLP for lid-driven Stokes flow in a wedge (Moffatt eddies).	Jnini et al. (2026)
PINN	Viscous Burgers	`create_viscous_burgers_task`	MLP for the 2+1-D viscous Burgers equation with closed-form targets.	Jnini et al. (2026)
PINN	Inviscid Burgers	`create_inviscid_burgers_task`	Two-network `MultiNet` with entropy consistency for the shock-forming inviscid Burgers law.	Jnini et al. (2026)
PINN	Euler (Sod)	`create_euler_task`	MLP for the 1-D compressible Euler shock tube; viscous warm-up + inviscid HLLC stages.	Jnini et al. (2026)
PINN	Stiff PK-PD	`create_pkpd_task`	MLP for a stiff pharmacokinetic–pharmacodynamic ODE (paclitaxel).	Jnini et al. (2026)

The physics-informed suite reproduces the benchmarks of Jnini et al. (2026), Curvature-aware optimization for high-accuracy physics-informed neural networks, arXiv:2604.05230.

Hydra task configurations¤

For large-scale studies, l2co-tasks ships ready-made Hydra config groups under l2co_tasks/conf/tasks/ (installed as package data). Each YAML describes a whole task distribution rather than a single task: an f3dasm sampler and domain (for example the grid over fn_name × dimensionality × seed), a data_generator pointing at the matching create_*_task factory, a feature schema, and the optimization bounds. Suites are provided for every family — bbob, bbob_small, bbob_two_functions (a minimal sphere + rastrigin pair where the best-performing optimizer differs per function), bbob_holdout, cec2005, quadratic, spirals, mnist1d, gaussian_classification, gaussian_meta, and one per PINN problem (helmholtz, stokes, viscous_burgers, inviscid_burgers, euler, pkpd, pde).

Downstream applications such as l2co_experiments consume these by adding l2co-tasks to the Hydra search path and selecting a suite by name:

# in your primary Hydra config
hydra:
  searchpath:
    - pkg://l2co_tasks.conf

defaults:
  - tasks: bbob          # any file in l2co_tasks/conf/tasks/

The selected suite can be overridden from the command line (e.g. ... tasks=cec2005) and materialized into an f3dasm.ExperimentData of Task objects via create_tasks_experimentdata(config=config.tasks, ...).

Community Support¤

If you find any issues, bugs or problems with this package, please use the GitHub issue tracker to report them.

License¤

This project is licensed under the BSD 3-Clause License. See LICENSE for the full license text.

This package is part of the L2CO ecosystem developed in the Bessa Research Group. The repositories below work together:

l2co — Learning to Choose Optimizers: a meta-learner that selects an optimizer from problem features before any evaluations, then reassesses that choice from the observed optimization trajectory.
rl2co — Reinforcement Learning to Choose Optimizers: a JAX-based RL agent that dynamically switches between optimizers during a run.
l2co-tasks — Optimization task definitions (BBOB, CEC 2005, PDE, spiral, …) compatible with the L2CO library.
l2co_experiments — Hydra + f3dasm experiment pipelines (dataset creation, training, rollouts, figures) for the L2CO studies.
agentic-l2co — An LLM-agent drop-in replacement for l2co.L2COModel, driving two-stage optimizer selection with an Ollama-hosted LLM.
bbob-jax — JAX implementations of the BBOB and CEC 2005 black-box optimization benchmark functions.
f3dasm — Framework for Data-Driven Design and Analysis of Structures and Materials; provides ExperimentData, pipelines, and SLURM orchestration.