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Quadratic programming QP involves minimizing or maximizing an objective function subject to bounds, linear equality, and inequality constraints. Example problems include portfolio optimization in finance, power generation optimization for electrical utilities, and design optimization in engineering. This example illustrates how to use the interior-point algorithm in quadprog on a portfolio optimization problem, and shows the algorithm running times on quadratic problems of different sizes. More elaborate analyses are possible by using features specifically designed for portfolio optimization. In this webinar, you will learn how MATLAB can be used to solve optimization problems. An example quadratic optimization problem is given, and the symbolic math tools in MATLAB are used to move from the governing equations to an objective function that can be evaluated.

Hi. great optimization problem and solution ! and very close to my job -> so i bought symobolic math toolbox and start to work on this example I tried to adapt HydroelectricDamOptimization_largeScale to my problem a 1 year problem with calculation every 8 hours. I have a problem with my MATLAB code that solves a nonlinear quadratic problem with SQP algorithm Sequential quadratic programming but in the "QP-SUB PROBLEM" section of the code that i.

Quadratic Programming with MATLAB and quadprog This guide assumes that you have already installed the Optimization Toolbox for your version of MATLAB. Quadratic programming QP is the problem of optimizing a quadratic objective function and is one of the simplests form of non-linear programming. 1 The objective function can contain bilinear or up to second order polynomial terms, 2 and the constraints are.