qptools 1.0.2

Fast quadratic programming tools (with CUDA support)

Dependencies

• setuptools version 42 or later
• pybind11 2.6.1 or newer
• numpy
• CUDA Toolkits for CUDA supports

Installation

Install from source (recommended)

CPU version (Install the package without CUDA support)

git clone https://gitee.com/clawfish/qptools.git
cd qptools
pip install .

CUDA version

git clone https://gitee.com/clawfish/qptools.git
cd qptools
export CUDA_HOME=(your cuda path)
export USE_CUDA=1
pip install .

Install from pip

CPU version (Install the package without CUDA support)

pip install qptools

CUDA version

export CUDA_HOME=(your cuda path)
export USE_CUDA=1
pip install qptools

Solving Quadratic Programming Problems

qp1: Basic Quadratic Programming

$$ \min_{x} \frac12 x^TPx+q^Tx \ \text{s.t. } lb \leq x \leq rb, \ Gx \leq h $$

where $P$ is a $n\times n$ positive definite matrix, $q$ is an $n$-dimensional vector, $G$ is a $g\times n$ matrix, $h$ is a $g$-dimensional vector, and $lb,rb$ are $n$-dimensional vectors defining the lower and upper bounds for variable $x$.

qp2: Quadratic Programming with Standardized Variables

$$ \min_{x} \frac12 x^TPx+q^Tx \ \text{s.t. } lb \leq \frac{x}{\text{sum}(x)} \leq rb, \ Gx \leq h $$

where $P$ is a $(n,n)$ positive definite matrix, $q$ is an $n$-dimensional vector, $G$ is a $(g,n)$ matrix, $h$ is a $g$-dimensional vector, and $lb,rb$ are $n$-dimensional vectors defining the lower and upper bounds for standardized variable $\frac{x}{\sum(x)}$.

Example

• Python (CPU): ./example/cpu.ipynb
• Python (CUDA): ./example/cuda.ipynb
• C++ (CPU): ./c/core/test/test.cpp
• C++ (CUDA): ./c/core/test/test.cu