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Optimization toolkit for high-dimensional, non-differentiable problems.

Project description

hdim-opt: High-Dimensional Optimization Toolkit

A modern optimization suite for complex, high-dimensional problems. This package provides algorithms to accelerate convergence, including the QUASAR evolutionary algorithm and HDS non-uniform QMC sampler.


Installation

Installed via hdim-opt directly from PyPI:

pip install hdim-opt

QUASAR Optimizer (Quasi-Adaptive Search with Asymptotic Reinitialization)

QUASAR is a quantum-inspired evolutionary algorithm, efficient for minimizing complex high-dimensional, non-differentiable, and non-parametric objective functions.

  • Benefit: Statistically significant improvements in convergence speed and solution quality compared to contemporary optimizers.

  • Reference: See experimental trials and analysis: [https://arxiv.org/abs/2511.13843].

Quick Use Example:

import hdim_opt
import numpy as np

# define search space
n_dim = 100
bounds = [(-100,100)] * n_dim

def obj_func(x):
    y = np.sum(x**2)
    return y

# run QUASAR
solution, fitness = hdim_opt.quasar(func=obj_func, bounds=bounds)

HDS Sampler (Hyperellipsoid Density Sampling)

HDS is a non-uniform Quasi-Monte Carlo sampling method, specifically designed to exploit promising regions of the search space.

  • Benefit: Provides control over the sample distribution. Results in higher average optimization solution quality when used for population initialization compared to uniform QMC methods.

  • Reference: See experimental trials and analysis: [https://arxiv.org/abs/2511.07836].

Quick Use Example:

import hdim_opt

# define search space
n_dim = 2
bounds = [(0,1)] * n_dim

# optional weights
weights = {
            0 : {'center': 0.25, 'std': 0.33},
            1 : {'center': 0.25, 'std': 0.33}
            }

# generate HDS samples
hds_samples = hdim_opt.hds(n_samples=10000, bounds=bounds, 
                    weights=weights, verbose=True)

Additional functions include:

  • sobol() to generate uniform Sobol samples (via SciPy)
  • sensitivity() to perform Sobol sensitivity analysis (via SALib) (work in progress)

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