ECOS: An SOCP solver for embedded systems

By Alexander Domahidi, Eric King-Wah Chu, Stephen P. Boyd. 2013

834 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Theory and Mathematics, Numerical Analysis

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Citations in the corpus

Variational Bayesian Reinforcement Learning with Regret Bounds (2018, methodology): “Problem (10) is an exponential cone program and can be solved efficiently using modern methods [32, 33, 15, 50, 31].”
OSQP: an operator splitting solver for quadratic programs (2017, methodology): “ECOS isgenerallyslowerthantheotherinterior-pointsolversbutfasterthanqpOASES thatshowsissueswithmanyconstraints.”
Stochastic matrix games with bandit feedback (2020, background): “The optimization problem in algorithm 3 is convex and can be expressed as an exponential cone program, for which efﬁcient algorithms exist [27, 38, 15].”
Model predictive control: Recent developments and future promise (2014, background): “Domahidi, Chu, and Boyd (2013) describe software that allows one to define a sparse second-order cone program for convex optimization problems that ismore general than a standard quadratic program and generates very short C++ code that does not rely on any external linear algebra libraries and can…”
acados—a modular open-source framework for fast embedded optimal control (2019, background): “1 PQP 2008 [20] QP proprietary CVXGEN 2009 [67] LP, QP proprietary ACADO Codegen 2009 [49] NMPC LGPL v3 FiOrdOs 2011 [89] QP GPL v3 muAO-MPC 2013 [99] LQMPC 3-clause BSD FORCES 2011 [24] QP, QCQP proprietary ECOS 2013 [23] SOCP GPL v3 GRAMPC 1.”
Fast online deconvolution of calcium imaging data (2016, background): “ompared the run time of our algorithm to a variety of state of the art convex solvers that can all be conveniently called from the convex optimization toolbox CVXPY [39]: embedded conic solver (ECOS, [40]), MOSEK [41], splitting conic solver (SCS, [42]) and GUROBI [43]. ECOS and MOSEK are the most c”
Augmented minimax linear estimation (2017, methodology): “We computed minimax linear weights via the cone solver ECOS (Domahidi, Chu, and Boyd, 2013), available in R via the package CVXR (Fu et al., 2017).”
On the Total Energy Efficiency of Cell-Free Massive MIMO (2017, background): “Solvers for such a convex problem do exist [27], but our numerical experiments reveal that they are not scalable with the problem size.”
Solving Mixed Integer Programs Using Neural Networks (2020, methodology): “The true objective values were found by solving the same LPs using the high-accuracy ECOS interior point solver (Domahidi et al. 2013) with parser-solver CVXPY (Diamond and Boyd 2016).”
Differentiable Convex Optimization Layers (2019, methodology): “These data are supplied to a conic solver to obtain a solution; there are many high-quality implementations of conic solvers ( e.g. , [64, 9, 32]).”
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks (2019, background): “, 2018] or study the dual of the original nonconvex verification problem [Dvijotham et al., 2018b,a, Qin et al., 2019]. In this paper, we unify both views in a common convex relaxation framework for NN verification, clarifying their relationships (as summarized in Fig. 1). Raghunathan et al. [2018b]”
Dynamic Locomotion in the MIT Cheetah 3 Through Convex Model-Predictive Control (2018, background): “Recent progress on convex optimization [21] and applications to model predictive control [22] has led to the development of many open source solvers such as ECOS [23] and qpOASES [24] that enable model predictive control problems to be solved rapidly and reliably.”