Scalar optimization - optim_doc
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## 1 Functions for optimization of a scalar objective function

Frontend

- nonlin_min: Interface for scalar non-linear optimization.

Backends

- lm_feasible: L/M-like optimizer, constraints met throughout optimization.
- octave_sqp: A wrapper to core Octaves sqp function.
- siman: Simulated annealing with constraints.
- d2_min: Newton-like optimizer, no constraints.

Alternative frontend

- fmins: Frontend for direct search (gradient-less) algorithms.

Standalone functions

- nmsmax: A Nelder-Mead simplex algorithm.
- mdsmax: A multidirectional search algorithm.
- adsmax: An alternating directions algorithm.
- nelder_mead_min: Another Nelder-Mead algorithm.
- powell: Direction-set (Powell's) method.
- standalone d2_min: Unconstrained Newton-like minimization, standalone function.
- bfgsmin: Unconstrained BFGS algorithm.
- nrm: Newton-Raphson algorithm.
- cg_min: A conjugate gradient method.
- brent_line_min: Linesearch, Brent method.
- line_min: Linesearch (minimize a function along dx).
- samin: A simulated annealing algorithm.
- de_min: A differential evolution algorithm.
- battery: Repeatedly call bfgsmin.