YALMIP : a toolbox for modeling and optimization in MATLAB

By Johan Efberg. 2004

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Citations in the corpus

Sever: A Robust Meta-Algorithm for Stochastic Optimization (2018, methodology): “We implemented the latter as a quadratic program, using Gurobi [Gur16] as a backend solver and YALMIP [L¨of04] as the modeling language.”
Certified Defenses for Data Poisoning Attacks (2017, methodology): “…+1 , − 1 } and take the one with higher loss: The results of Algorithm 1 are given in Figures 2a and 2b; here and elsewhere, we used a combination of CVXPY (Diamond and Boyd, 2016), YALMIP (Löfberg, 2004), SeDuMi (Sturm, 1999), and Gurobi (Gurobi Optimization, Inc., 2016) to solve the optimization.”
Certified Defenses against Adversarial Examples (2018, methodology): “In our experiments, we use the modeling toolbox YALMIP (Löfberg, 2004) with Sedumi (Sturm, 1999) as a backend to solve the SDPs, using the dual form (14); this took roughly 10 minutes per SDP (around 8 hours in total for a given model).”
Semidefinite relaxations for certifying robustness to adversarial examples (2018, methodology): “We use the YALMIP toolbox [19] with MOSEK as a backend to solve the different convex programs that arise in these certification procedures.”
Survey of convex optimization for aerospace applications (2017, background): “are also available for modeling a problem in a simple and natural language, such as YALMIP [60], CVX [61],”
A Sufficient Condition for Power Flow Insolvability With Applications to Voltage Stability Margins (2012, methodology): “Using optimization codes YALMIP [29] and SeDuMi [30], we obtained a solution to (14) for the three-bus system with rank (null (A)) = 4.”
CVXGEN: a code generator for embedded convex optimization (2011, methodology): “…interior point methods for more general canonical convex problems, such as the cone programs used in CVX (Grant and Boyd 2008a) or YALMIP (Löfberg 2004) (in both cases, by SeDuMi (Sturm and Using 1999) or SDPT3 (Toh et al. 1999) ), are very similar, with just a few differences in the…”
Explicit Model-Predictive Control of a PWM Inverter With an LCL Filter (2009, methodology): “The optimal control problem is formulated and solved using Yalmip [22] and MPT [23] toolboxes in Matlab.”
Learning an Approximate Model Predictive Controller With Guarantees (2018, background): “The LMI’s can be solved with Yalmip [47].”
A Rewriting System for Convex Optimization Problems (2017, background): “The ruleset employed by CVXPY, CVX, Convex.jl, and Yalmip is called disciplined convex programming (DCP) (Grant, 2004).”
Application of the Moment-SOS Approach to Global Optimization of the OPF Problem (2013, methodology): “The materials used are: • Data of WB2, LMBM3, WB5 systems available online [9], • Intel(R) Xeon(TM) MP CPU 2.70 GHz 7.00 Go RAM, • MATLAB version 7.7 2008b, • MATLAB-package MATPOWER version 3.2 [37], • SeDuMi 1.02 [33] with tolerance parameter pars.eps set to 10−12 for all computa- tions, • MATLAB-”
Computing in Operations Research Using Julia (2013, background): “YALMIP and Pyomo implement the operator overloading approach, while CVX implements the string-parsing approach.”
On H ∞ and H 2 performance regions of multi-agent systems ✩ (2011, unknown): “Solving LMI (12) with γ = 1 by using toolboxes Yalmip (Löfberg, 2004) and SeDuMi (Sturm, 1999) give a feasible solution: P = 1.239 0.1371 0.1371 0.2015 and τ = 1.5325.”
Real-Time Convex Optimization in Signal Processing (2010, methodology): “Recently developed parser-solvers , such as YALMIP [66], CVX [67], CVXMOD [68], and Pyomo [69] automate this reduction process.”