Quadratic programming is the mathematical problem of finding a vector x that minimizes a quadratic function: minx{12xTHx+fTx} Subject to the constraints: Ax≤b(inequality constraint)Aeqx=beq(equality constraint)lb≤x≤ub(bound constraint)
For example, for solving an equality constrained quadratic programming problem in the form of (16a)minimizexTAx∕2+aTx, (16b)subject toDx=b. Then, an error function can be design as (17)Φ(u)=‖Dx−b‖22∕2+‖Ax+DTy+a‖22∕2. A gradient-based neural network model can be ...
example x = quadprog(problem) returns the minimum for problem, a structure described in problem. Create the problem structure using dot notation or the struct function. Alternatively, create a problem structure from an OptimizationProblem object by using prob2struct. example [x,fval] = quadprog(_...
The matrices that define the problems in this example are dense; however, the interior-point algorithm in quadprog can also exploit sparsity in the problem matrices for increased speed. For a sparse example, see Large Sparse Quadratic Program with Interior Point Algorithm....
Example showing solver-based quadratic programming on a basic portfolio model. Problem-Based Second-Order Cone Programming Minimize Energy of Piecewise Linear Mass-Spring System Using Cone Programming, Problem-Based Presents a problem-based example of cone programming. ...
For a solver-based version of this example, see Bound-Constrained Quadratic Programming, Solver-Based. Problem Definition Consider building a circus tent to cover a square lot. The tent has five poles covered with a heavy, elastic material. The problem is to find the natural shape of the tent...
y = apm_quadprog(H,f,A,b,Aeq,beq,LB,UB,X0) writes a quadratic programming model in APMonitor Modeling Language and attempts to solve the quadratic programming problem: min 0.5*x'*H*x + f'*x subject to: A*x <= b, Aeq*x = beq ...
Quadratic programming problemQuadratic programming is potentially capable of strategic decision making in real world problems. However, practical problems rarely conform to crisp parameters, and hence the prospects of these problems with inexact parameters are inevitably higher. The existing studies regarding...
We study the Bipartite Boolean Quadratic Programming Problem (BBQP) which is an extension of the well known Boolean Quadratic Programming Problem (BQP). Applications of the BBQP include mining discrete patterns from binary data, approximating matrices by rank-one binary matrices, computing the cut-...
Optimization in MATLAB: An Introduction to Quadratic Programming 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...