# Documentation

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## Hessian

### fminunc Hessian

The Hessian for an unconstrained problem is the matrix of second derivatives of the objective function f:

• Quasi-Newton Algorithm`fminunc` returns an estimated Hessian matrix at the solution. It computes the estimate by finite differences.

• Trust-Region Algorithm`fminunc` returns a Hessian matrix at the next-to-last iterate.

• If you supply a Hessian in the objective function, `fminunc` returns this Hessian.

• If you supply a `HessMult` function, `fminunc` returns the `Hinfo` matrix from the `HessMult` function. For more information, see `HessMult` in the `trust-region` section of the `fminunc` `options` table.

• Otherwise, `fminunc` returns an approximation from a sparse finite difference algorithm on the gradients.

This Hessian is accurate for the next-to-last iterate. However, the next-to-last iterate might not be close to the final point.

The reason the `trust-region` algorithm returns the Hessian at the next-to-last point is for efficiency. `fminunc` uses the Hessian internally to compute its next step. When `fminunc` reaches a stopping condition, it does not need to compute the next step, so does not compute the Hessian.

### fmincon Hessian

The Hessian for a constrained problem is the Hessian of the Lagrangian. For an objective function f, nonlinear inequality constraint vector c, and nonlinear equality constraint vector ceq, the Lagrangian is

`$L=f+\sum _{i}{\lambda }_{i}{c}_{i}+\sum _{j}{\lambda }_{j}ce{q}_{j}.$`

The λi are Lagrange multipliers; see First-Order Optimality Measure and Lagrange Multiplier Structures. The Hessian of the Lagrangian is

`$H={\nabla }^{2}L={\nabla }^{2}f+\sum _{i}{\lambda }_{i}{\nabla }^{2}{c}_{i}+\sum _{j}{\lambda }_{j}{\nabla }^{2}ce{q}_{j}.$`

`fmincon` has four algorithms, with several options for Hessians, as described in fmincon Trust Region Reflective Algorithm, fmincon Active Set Algorithm, and fmincon Interior Point Algorithm. `fmincon` returns the following for the Hessian:

• `active-set` or `sqp` Algorithm`fmincon` returns the Hessian approximation it computes at the next-to-last iterate. `fmincon` computes a quasi-Newton approximation of the Hessian matrix at the solution in the course of its iterations. This approximation does not, in general, match the true Hessian in every component, but only in certain subspaces. Therefore the Hessian that `fmincon` returns can be inaccurate. For more details of the `active-set` calculation, see SQP Implementation.

• `trust-region-reflective` Algorithm`fmincon` returns the Hessian it computes at the next-to-last iterate.

• If you supply a Hessian in the objective function, `fmincon` returns this Hessian.

• If you supply a `HessMult` function, `fmincon` returns the `Hinfo` matrix from the `HessMult` function. For more information, see Trust-Region-Reflective Algorithm in `fmincon` `options`.

• Otherwise, `fmincon` returns an approximation from a sparse finite difference algorithm on the gradients.

This Hessian is accurate for the next-to-last iterate. However, the next-to-last iterate might not be close to the final point.

The reason the `trust-region-reflective` algorithm returns the Hessian at the next-to-last point is for efficiency. `fmincon` uses the Hessian internally to compute its next step. When `fmincon` reaches a stopping condition, it does not need to compute the next step, so does not compute the Hessian.

• `interior-point` Algorithm

• If the `Hessian` option is `lbfgs` or `fin-diff-grads`, or if you supply a Hessian multiply function (`HessMult`), `fmincon` returns `[]` for the Hessian.

• If the `Hessian` option is `bfgs` (the default), `fmincon` returns a quasi-Newton approximation to the Hessian at the final point. This Hessian can be inaccurate, as in the `active-set` or `sqp` algorithm Hessian.

• If the `Hessian` option is `user-supplied`, `fmincon` returns the user-supplied Hessian at the final point.