YALMIP : a toolbox for modeling and optimization in MATLAB

By Johan Löfberg. 2004

3,659 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/d3a6296eb2b61102e8071e93d32fd3c727057ee7

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In Control and Systems Engineering

In Numerical Analysis

Citations in the corpus

Security Constrained Multi-Stage Transmission Expansion Planning Considering a Continuously Variable Series Reactor (2017, methodology): “The MILP problem is modeled using the YALMIP [36] toolbox in MATLAB with the CPLEX solver [37] selected to solve the model.”
Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework (2016, methodology): “ + jju kjj22, an horizon length N= 4, and the states and input constraints (58d)-(58e). The LMPC (23) and (25) is reformulated as a Mixed Integer Quadratic Programming and it is implemented in YALMIP [20] using the solver bonmin [21]. Each j-th iteration has an unknown ﬁxed-time duration, t~ j, deﬁn”
Decentralised static output feedback stabilisation and synchronisation of networks (2009, methodology): “LMIRank can be called using YALMIP [24], a MATLAB toolbox for rapid prototyping of optimization algorithms.”
Gain-scheduled H 2 and H ∞ control of discrete-time polytopic time-varying systems (2010, methodology): “Control, 1997, 42, (7), pp. 896–911 [35] LÖFBERG J.: ‘YALMIP: a toolbox for modeling and optimization in MATLAB’.”
Linear Matrix Inequalities in Control (2011, background): “For example, Yalmip [12] is a very flexible and non-commercial toolbox for defining and solving advanced optimization problems.”
Chance-Constrained Optimal Path Planning With Obstacles (2011, methodology): “We use the YALMIP interface for Matlab[47], with CPLEX[6] used to solve the MILPs generated by the FRR and FRT steps, and with SNOPT[48] used to solve the nonlinear convex programs generated by the risk allocation steps.”
LMI Techniques for Optimization Over Polynomials in Control: A Survey (2010, methodology): “implemented in the MATLAB toolboxes GloptiPoly [36], SOSTOOLS [37] and YALMIP [38].”
DELAY-DEPENDENT STABILITY ANALYSIS OF LINEAR TIME DELAY SYSTEMS (2006, methodology): “The computations are performed under MATLAB c using the SDP solver SeDuMi (Sturm, 1999) and the parser YALMIP (Löfberg, 2004).”
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.”