Xopt

Flexible optimization of arbitrary problems in Python.

The goal of this package is to provide advanced algorithmic support for arbitrary optimization problems (simulations/control systems) with minimal required coding. Users can easily connect arbitrary evaluation functions to advanced algorithms with minimal coding with support for multi-threaded or MPI-enabled execution.

Currenty Xopt provides:

• Optimization algorithms:
• Genetic algorithms
  • cnsga Continuous NSGA-II with constraints
• Bayesian optimization (BO) algorithms:
  • upper_confidence_bound BO using Upper Confidence Bound acquisition function (w/ or w/o constraints, serial or parallel)
  • expected_improvement BO using Expected Improvement acquisition function (w/ or w/o constraints, serial or parallel)
  • mobo Multi-objective BO (w/ or w/o constraints, serial or parallel)
  • bayesian_exploration Autonomous function characterization using Bayesian Exploration
  • mggpo Parallelized hybrid Multi-Generation Multi-Objective Bayesian optimization
  • multi_fidelity Multi-fidelity single or multi objective optimization
  • BAX Bayesian algorithm execution using virtual measurements
  • BO customization:
  • Trust region BO
  • Heteroskedastic noise specification
  • Multiple acquisition function optimization stratigies
• extremum_seeking Extremum seeking time-dependent optimization
• rcds Robust Conjugate Direction Search (RCDS)
• neldermead Nelder-Mead Simplex
• Sampling algorithms:
• random Uniform random sampling
• Convenient YAML/JSON based input format
• Driver programs:
• xopt.mpi.run Parallel MPI execution using this input format

Xopt does not provide: - your custom simulation via an evaluate function.

Rather, Xopt asks you to define this function.

Getting Started

Xopt Overview PDF gives an overview of Xopt's design and usage.

Xopt Built-In Generators provides a list of available algorithms implemented in the Xopt Generator framework.

Simple Bayesian Optimization Example shows Xopt usage for a simple optimization problem.

Xopt IPAC23 paper summarizes the usage of Xopt in particle accelerator physics problems.

Configuring an Xopt run

Xopt runs can be specified via a YAML file or dictonary input. This requires generator, evaluator, and vocs to be specified, along with optional general options such as max_evaluations. An example to run a multi-objective optimiation of a user-defined function my_function is:

generator:
    name: cnsga
    population_size: 64
    population_file: test.csv
    output_path: .

evaluator:
    function: my_function
    function_kwargs:
      my_arguments: 42

vocs:
    variables:
        x1: [0, 3.14159]
        x2: [0, 3.14159]
    objectives: 
        y1: MINIMIZE
        y2: MINIMIZE
    constraints:
        c1: [GREATER_THAN, 0]
        c2: [LESS_THAN, 0.5]
    constants: {a: dummy_constant}

max_evaluations: 6400    

Xopt can also be used through a simple Python interface.

import math

from xopt.vocs import VOCS
from xopt.evaluator import Evaluator
from xopt.generators.bayesian import UpperConfidenceBoundGenerator
from xopt import Xopt

# define variables and function objectives
vocs = VOCS(
    variables={"x": [0, 2 * math.pi]},
    objectives={"f": "MINIMIZE"},
)

# define the function to optimize
def sin_function(input_dict):
    return {"f": math.sin(input_dict["x"])}

# create Xopt evaluator, generator, and Xopt objects
evaluator = Evaluator(function=sin_function)
generator = UpperConfidenceBoundGenerator(vocs=vocs)
X = Xopt(evaluator=evaluator, generator=generator, vocs=vocs)

# call X.random_evaluate() to generate + evaluate 3 initial points
X.random_evaluate(3)

# run optimization for 10 steps
for i in range(10):
    X.step()

# view collected data
print(X.data)

Defining an evaluation function

Xopt can interface with arbitrary evaluate functions (defined in Python) with the following form:

def evaluate(inputs: dict) -> dict:
    """ your code here """

Evaluate functions must accept a dictionary object that at least has the keys specified in variables, constants and returns a dictionary containing at least the keys contained in objectives, constraints. Extra dictionary keys are tracked and used in the evaluate function but are not modified by xopt.

Using MPI

Example MPI run, with xopt.yaml as the only user-defined file:

mpirun -n 64 python -m mpi4py.futures -m xopt.mpi.run xopt.yaml

Citing Xopt

If you use Xopt for your research, please consider adding the following citation to your publications.

R. Roussel., et al., "Xopt: A simplified framework for optimization of accelerator problems using advanced algorithms", 
in Proc. IPAC'23, Venezia.doi:https://doi.org/10.18429/JACoW-14th International Particle Accelerator Conference-THPL164

BibTex entry:

@inproceedings{Xopt,
    title        = {Xopt: A simplified framework for optimization of accelerator problems using advanced algorithms},
    author       = {R. Roussel and A. Edelen and A. Bartnik and C. Mayes},
    year         = 2023,
    month        = {05},
    booktitle    = {Proc. IPAC'23},
    publisher    = {JACoW Publishing, Geneva, Switzerland},
    series       = {IPAC'23 - 14th International Particle Accelerator Conference},
    number       = 14,
    pages        = {4796--4799},
    doi          = {doi:10.18429/jacow-ipac2023-thpl164},
    isbn         = {978-3-95450-231-8},
    issn         = {2673-5490},
    url          = {https://indico.jacow.org/event/41/contributions/2556},
    paper        = {THPL164},
    venue        = {Venezia},
    language     = {english}
}

Particular versions of Xopt can be cited from Zenodo