hdim-opt 1.0.1

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

hdim-opt: High-Dimensional Optimization Toolkit

A modern optimization suite for complex, high-dimensional problems. This package provides state-of-the-art algorithms to accelerate convergence and intelligently explore the search space, including an evolutionary optimizer and a Quasi-Monte Carlo sampler.

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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, highly 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.

Quick Use Example:

import hdim_opt
import numpy as np

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

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

# 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, and 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 = 100
bounds = [(-100,100)] * n_dim
n_samples = 1000

# generate HDS samples
hds_samples = hdim_opt.hds(n_samples, bounds)