README
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Function optimization based on Nelder-Mead's downhill simplex (1965)
This package, nmoptim implements a variant of the Nelder-Mead downhill simplex optimization heuristic. The idea is that we have a function we want to optimize and a simplex in a search space. The algorithm iteratively changes the simplex through contractions, expansions and reflections until the simplex is extremely small and contains the optimal minimum point in the function.
It doesn't work for functions with several optimal points and there are other problems with the approach (infinite cycling). But in many cases, the method works.
License
Copyright 2014-2020 Jesper Louis Andersen
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Documentation
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Index ¶
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func Optimize ¶
func Optimize(f func([]float64) float64, start [][]float64, cf func([]float64)) ([]float64, int, int)
Optimize function f with Nelder-Mead. start points to a slice of starting points It is the responsibility of the caller to make sure the dimensionality is correct.
The cf function is a constraint function which can be used to clamp the space in which your optimization runs. This avoid the algorithm picking extreme points, ouside the space you want to operate on.
Types ¶
This section is empty.
Source Files