YALMIP : A toolbox for modeling and optimization in MATLAB

1,558 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Output feedback model predictive control of uncertain norm‐bounded linear systems (2011, methodology): “The MPC algorithm NB-OUT has been solved using the SDP solver SeDuMi for N =1,2 by means of the yalmip parser [24].”
Distributionally Robust Logistic Regression (2015, methodology): “s We now showcase the power of distributionally robust logistic regression in simulated and empirical experiments. All optimization problems are implemented in MATLAB via the modeling language YALMIP [27] and solved with the state-of-the-art nonlinearprogramming solver IPOPT [28]. All experiments we”
Certifiably Optimal Low Rank Factor Analysis (2016, methodology): “Because speed and the need for warmstarts are not only desirable but necessary for our purposes, we choose to use a FOM approach to solving (48).”
Zero Duality Gap in Optimal Power Flow Problem (2012, background): “, Optimization 4), which is an SDP problem [29].”
Optimal Designs for Rational Function Regression (2010, methodology): “For practical computations several Matlab toolboxes, such as CVX [17] and YALMIP [27], are available to facilitate the translation of semideﬁnite programs such as the one above to the the so-called “standard form” required by semideﬁnite solvers.”