osqp

osqp_benchmarks

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osqp / osqp_benchmarks

osqp_benchmarks

QP Benchmarks for the OSQP Solver against GUROBI, MOSEK, ECOS and qpOASES

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QP Benchmarks for the OSQP Solver against GUROBI, MOSEK, ECOS and qpOASES

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yimingyan/qp-test-problems · linked in 2 of the same discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

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Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

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Observed mentions: 2017-11-26 17:32:51 UTC to 2022-05-01 08:42:47 UTC.

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Comments
2017	2017-01-01 00:00:00 UTC	2018-01-01 00:00:00 UTC	5	5
2018	2018-01-01 00:00:00 UTC	2019-01-01 00:00:00 UTC	0	0
2019	2019-01-01 00:00:00 UTC	2020-01-01 00:00:00 UTC	0	0
2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	0	0
2021	2021-01-01 00:00:00 UTC	2022-01-01 00:00:00 UTC	0	0
2022	2022-01-01 00:00:00 UTC	2023-01-01 00:00:00 UTC	2	2

What Hacker News says

7 sources

SparseRunner·May 1, 2022

OSQP is fast, but is only for QP, not LP. The "benchmarks" (https://github.com/osqp/osqp_benchmarks) include some important problem classes but are random so, for general QP, are not valid. On the industry standard benchmarks (http://plato.asu.edu/ftp/qpbench.html) OSQP doesn't look so good, and it's not even tested against commercial solvers (http://plato.asu.edu/ftp/cconvex.html). Our experience with it on general benchmarking problems is that it can struggle to get sufficiently accurate dual values to the extent that it fails to solve them. For certain classes of important QP problems, and when optimization to small tolerances is not required, it's undoubtedly a great solver - but it's not a general solver.

In Optimization solvers: missing link for fully open-source energy system modeling

rwlincoln·Apr 30, 2022

I am surprised not to see any mention of the OSQP (Operator Splitting Quadratic Program) solver. It is the most impressive open source solver of this type that I have seen published in recent years. It appears to have been developed as a collaboration between Princeton, ETH Zurich, Oxford, Stanford and some other prestigious names. The benchmarks show that it compares favorably with leading proprietary solvers: https://github.com/osqp/osqp_benchmarks The problem described seems to be an ideal use-case for Machine Learning. The MATPOWER Optimal Scheduling Toolkit (MOST) can already solve: "a stochastic, security-constrained, combined unit-commitment and multiperiod optimal power flow problem with locational contingency and load-following reserves, ramping costs and constraints, deferrable demands, lossy storage resources and uncertain rene

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wable generation." Much more and it becomes a global optimization problem where you can never really be sure you are not just stuck in a local optimum. The L2RPN (Learning to Run a Power Network) challenge, from RTE-France, is the most interesting effort I have seen applying Machine Learning to energy system management. https://l2rpn.chalearn.org/ https://github.com/rte-france/l2rpn-baselines The competition has been renewed for 2022 and has been accepted for the IEEE World Congress on Computational Intelligence in July. https://wcci2022.org

In Optimization solvers: missing link for fully open-source energy system modeling

tmyklebu·Nov 28, 2017

> We compared our OSQP solver to other general-purpose solvers. That's why we chose GUROBI and Mosek. Your computational results don't support calling OSQP general-purpose. OSQP does okay on the easy problems you randomly generate, but this doesn't seem to generalise---OSQP gives results on a bunch of the Maros-Meszaros problems that markedly disagree with previously published work. > However our point is not to compare our solver to specific methods for certain classes (like libsvm). You may not want to compare against libsvm's solver (though I imagine you look pretty good on the problems you're generating) but there is also a lot of data available at the libsvm webpage. This data might serve as a more representative SVM testbed than the random easy instances you're creating. > In the benchmarks we are comparing examples with randomly

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generated data because we were interested in ~1400 problems in a range from 100 to 10 million nonzeros. The examples are not just random QPs, but data are generated in a reasonable manner and they have the same structure as problems people would actually solve in practice. The "random QPs" and "random equality QPs" do seem to be "just random QPs." I get cond(A^T A) ranging between 1.3 to 1.7 with your Lasso problem generator. These instances are going to be incredibly easy to solve, even with very naive algorithms. (Separately, why not eliminate the 'y' variable from your formulation? You get a formulation that's about 100 times smaller, and you should see a corresponding speedup.) > In the arXiv paper we described in detail how we generated and checked optimality for the examples. Yes, you check for a bunch of residuals' infinity-norms being smaller than 1e-3. But you get results that differ markedly from the other solvers' results. To me, that raises the question of whether the 1e-3 test is appropriate. > As mentioned in the README of the repository that you checked out (https://github.com/oxfordcontrol/osqp_benchmarks) we used the conversion scripts from here: https://github.com/YimingYAN/QP-Test-Problems > > If you know better ways to convert the Maros problems (or other QP/LP benchmarks) to mat files or other formats compatible with Python, please let me know. I would be happy to try them. Well, QPS is a punch-card style input format like MPS. Like MPS, I imagine you can write a parser for it in a couple of highly unfulfilling hours.

In Show HN: OSQP: a new first-order solver for large-scale quadratic programs

sidereus·Nov 27, 2017

We compared our OSQP solver to other general-purpose solvers. That's why we chose GUROBI and Mosek. There are also examples of control problems for which it is possible to design very efficient tailored methods. However our point is not to compare our solver to specific methods for certain classes (like libsvm). In the benchmarks we are comparing examples with randomly generated data because we were interested in ~1400 problems in a range from 100 to 10 million nonzeros. The examples are not just random QPs, but data are generated in a reasonable manner and they have the same structure as problems people would actually solve in practice. In the arXiv paper we described in detail how we generated and checked optimality for the examples. As mentioned in the README of the repository that you checked out (https://github.com/oxfordcontrol/osqp

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_benchmarks) we used the conversion scripts from here: https://github.com/YimingYAN/QP-Test-Problems If you know better ways to convert the Maros problems (or other QP/LP benchmarks) to mat files or other formats compatible with Python, please let me know. I would be happy to try them.

In Show HN: OSQP: a new first-order solver for large-scale quadratic programs

sidereus·Nov 27, 2017

Which version of OSQP did you try for? 0.2 has improved a lot compared to previous versions (much better convergence behavior with adaptive rho). Even though OSQP can solve also LPs, it is optimized to solve QPs. You can find some benchmarks here: https://github.com/oxfordcontrol/osqp_benchmarks They include: - Randomly generated QPs from several classes such as control, portfolio, lasso, svm, Huber fitting - The hard QP problems from the Maros-Meszaros test class (the reference benchmark testset for convex QPs)

In Show HN: OSQP: a new first-order solver for large-scale quadratic programs

sidereus·Nov 26, 2017

I am happy to compare IPOPT as well. There was a github issue for this: https://github.com/oxfordcontrol/osqp_benchmarks/issues/1 As I said there, the current IPOPT Python interfaces do not report the timings. If you are aware of any fair way to time it from Python, I would be happy to add it. In my opinion using the Python "time" module can cause lags due to the Python interpreter.

In Show HN: OSQP: a new first-order solver for large-scale quadratic programs

sidereus·Nov 26, 2017

arXiv paper: https://arxiv.org/abs/1711.08013 benchmarks: https://github.com/oxfordcontrol/osqp_benchmarks It is the default LP/QP solver in cvxpy 1.0: https://github.com/cvxgrp/cvxpy

In Show HN: OSQP: a new first-order solver for large-scale quadratic programs