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Octave can also solve Quadratic Programming problems, this is
min 0.5 x'*H*x + x'*q
subject to
A*x = b lb <= x <= ub A_lb <= A_in*x <= A_ub
Solve the quadratic program
min 0.5 x'*H*x + x'*q x
subject to
A*x = b lb <= x <= ub A_lb <= A_in*x <= A_ub
using a null-space active-set method.
Any bound (A, b, lb, ub, A_lb,
A_ub) may be set to the empty matrix ([]
) if not
present. If the initial guess is feasible the algorithm is faster.
An optional structure containing the following parameter(s) used to define the behavior of the solver. Missing elements in the structure take on default values, so you only need to set the elements that you wish to change from the default.
MaxIter (default: 200)
Maximum number of iterations.
Structure containing run-time information about the algorithm. The following fields are defined:
solveiter
The number of iterations required to find the solution.
info
An integer indicating the status of the solution.
The problem is feasible and convex. Global solution found.
The problem is not convex. Local solution found.
The problem is not convex and unbounded.
Maximum number of iterations reached.
The problem is infeasible.
Minimize 1/2*x'*c*x + d'*x
subject to x >= 0
. c
and d must be real, and c must be symmetric and positive
definite. x0 is an optional initial guess for x.
Outputs:
The minimum attained model value, 1/2*xmin’*c*xmin + d’*xmin
An indicator of convergence. 0 indicates that the iteration count was exceeded, and therefore convergence was not reached; >0 indicates that the algorithm converged. (The algorithm is stable and will converge given enough iterations.)
A structure with two fields:
"algorithm"
: The algorithm used ("nnls"
)
"iterations"
: The number of iterations taken.
Not implemented.
Next: Nonlinear Programming, Previous: Linear Programming, Up: Optimization [Contents][Index]