| # Construct random matrix P with specified condition number | |
| # | |
| # Bierlaire, M., Toint, P., and Tuyttens, D. (1991). | |
| # On iterative algorithms for linear ls problems with bound constraints. | |
| # Linear Algebra and Its Applications, 143, 111–143. | |
| # | |
| cond_P = 10**2 # Condition number | |
| log_cond_P = np.log(cond_P) | |
| exp_vec = np.arange(-log_cond_P/4., log_cond_P * (n + 1)/(4 * (n - 1)), log_cond_P/(2.*(n-1))) |
| \usepackage{booktabs} | |
| \usepackage{csvsimple} | |
| ... | |
| ... | |
| \begin{table} | |
| \centering | |
| \caption{Example table.} | |
| \label{tab:tablelabel} | |
| \begin{tabular}{ll} |
| using ProgressMeter | |
| using Distributed | |
| addprocs(2) | |
| @everywhere using LinearAlgebra | |
| @everywhere function myfunc(theta) | |
| for i in 1:100000 | |
| dot(theta, theta) | |
| end | |
| return Dict(["A" => norm(theta, 2), "B" => norm(theta, 1)]) |
| \begin{figure} | |
| \begin{tikzpicture} | |
| \begin{axis} | |
| [ | |
| axis x line=bottom, | |
| axis y line=left, | |
| enlarge y limits=0.1, % Enlarge limits by 10% | |
| % ymode=log, % If you want to be logarithmic | |
| width=\textwidth, | |
| height=0.33\textheight, |
| <h1>Privacy Policy</h1> | |
| <p>Effective date: March 11, 2019</p> | |
| <p>MITaly - Italian Association at MIT ("us", "we", or "our") operates the http://mitaly.mit.edu website (the "Service").</p> | |
| <p>This page informs you of our policies regarding the collection, use, and disclosure of personal data when you use our Service and the choices you have associated with that data. Our Privacy Policy for MITaly - Italian Association at MIT is created with the help of the <a href="https://www.freeprivacypolicy.com/">Free Privacy Policy website</a>.</p> |
| ERROR: Test grad for partial minimization/maximization problems. | |
| ---------------------------------------------------------------------- | |
| Traceback (most recent call last): | |
| File "/Users/sidereus/Dropbox/research/code/projects/cvxpy/cvxpy/tests/test_grad.py", line 692, in test_partial_problem | |
| fix_prob.solve() | |
| File "/Users/sidereus/Dropbox/research/code/projects/cvxpy/cvxpy/problems/problem.py", line 271, in solve | |
| return solve_func(self, *args, **kwargs) | |
| File "/Users/sidereus/Dropbox/research/code/projects/cvxpy/cvxpy/problems/problem.py", line 511, in _solve | |
| self.unpack_results(solution, full_chain, inverse_data) | |
| File "/Users/sidereus/Dropbox/research/code/projects/cvxpy/cvxpy/problems/problem.py", line 650, in unpack_results |
| N = 8 | |
| FUNCTION: with_compile Used 100 times with solver ECOS | |
| Solver time | |
| MEDIAN 0.000482311 | |
| MEAN 0.000531244488888889 | |
| STDEV 0.0001849527009135786 | |
| CVXPY time | |
| MEDIAN 0.03360658268151855 | |
| MEAN 0.041804371728830295 | |
| STDEV 0.02494174225767466 |
| N = 8 | |
| FUNCTION: with_compile Used 100 times with solver ECOS | |
| Solver time | |
| MEDIAN 0.000491588 | |
| MEAN 0.000584584825 | |
| STDEV 0.00025277084914314066 | |
| CVXPY time | |
| MEDIAN 0.03191720293206787 | |
| MEAN 0.03357378209356384 | |
| STDEV 0.005317177566723467 |
| from __future__ import print_function | |
| import time | |
| import random | |
| import cvxpy as cvx | |
| import numpy as np | |
| solver = "ECOS" | |
| for N in [8, 800, 8000]: |
OSQP Contributor License Agreement
Adapted from https://www.apache.org/licenses/icla.pdf
Thank you for your interest in the OSQP project ("OSQP").
In order to clarify the intellectual property license granted with Contributions from any person or entity, OSQP must have a Contributor License Agreement ("CLA") on file that has been signed by each Contributor, indicating agreement to the license terms below. This license is for