Nelder–Mead method

From Wikipedia, the free encyclopedia
Jump to: navigation, search
Nelder Mead1.gif
Nelder Mead2.gif

Nelder–Mead simplex search over the Rosenbrock banana function (above) and Himmelblau's function (below)

See simplex algorithm for Dantzig's algorithm for the problem of linear optimization.

The Nelder–Mead method or downhill simplex method or amoeba method is a commonly used nonlinear optimization technique, which is a well-defined numerical method for problems for which derivatives may not be known. However, the Nelder–Mead technique is a heuristic search method that can converge to non-stationary points[1] on problems that can be solved by alternative methods.[2]

The Nelder–Mead technique was proposed by John Nelder & Roger Mead (1965) [3] and is a technique for minimizing an objective function in a many-dimensional space.


The method uses the concept of a simplex, which is a special polytope of N + 1 vertices in N dimensions. Examples of simplices include a line segment on a line, a triangle on a plane, a tetrahedron in three-dimensional space and so forth.

The method approximates a local optimum of a problem with N variables when the objective function varies smoothly and is unimodal.

For example, a suspension bridge engineer has to choose how thick each strut, cable, and pier must be. These elements are interdependent, but it is not easy to visualize the impact of changing any specific element. Simulation of such complicated structures is often extremely computationally expensive to run, possibly taking upwards of hours per execution. An engineer may therefore prefer the Nelder-Mead method as it requires fewer evaluations per iteration than other optimization methods.

Nelder–Mead generates a new test position by extrapolating the behavior of the objective function measured at each test point arranged as a simplex. The algorithm then chooses to replace one of these test points with the new test point and so the technique progresses. The simplest step is to replace the worst point with a point reflected through the centroid of the remaining N points. If this point is better than the best current point, then we can try stretching exponentially out along this line. On the other hand, if this new point isn't much better than the previous value, then we are stepping across a valley, so we shrink the simplex towards a better point.

Unlike modern optimization methods, the Nelder–Mead heuristic can converge to a non-stationary point unless the problem satisfies stronger conditions than are necessary for modern methods.[1] Modern improvements over the Nelder–Mead heuristic have been known since 1979.[2]

Many variations exist depending on the actual nature of the problem being solved. A common variant uses a constant-size, small simplex that roughly follows the gradient direction (which gives steepest descent). Visualize a small triangle on an elevation map flip-flopping its way down a valley to a local bottom. This method is also known as the Flexible Polyhedron Method. This, however, tends to perform poorly against the method described in this article because it makes small, unnecessary steps in areas of little interest.

One possible variation of the NM algorithm[edit]

  • 1. Order according to the values at the vertices:
f(\textbf{x}_{1}) \leq f(\textbf{x}_{2}) \leq \cdots \leq f(\textbf{x}_{n+1})
  • 2. Calculate \textbf{x}_{o}, the centroid of all points except \textbf{x}_{n+1}.
  • 3. Reflection
Compute reflected point \textbf{x}_r = \textbf{x}_o + \alpha (\textbf{x}_o - \textbf{x}_{n+1})
If the reflected point is better than the second worst, but not better than the best, i.e.: f(\textbf{x}_{1}) \leq f(\textbf{x}_{r}) < f(\textbf{x}_{n}),
then obtain a new simplex by replacing the worst point \textbf{x}_{n+1} with the reflected point \textbf{x}_{r}, and go to step 1.
  • 4. Expansion
If the reflected point is the best point so far, f(\textbf{x}_{r}) < f(\textbf{x}_{1}),
then compute the expanded point \textbf{x}_{e} = \textbf{x}_o + \gamma (\textbf{x}_o - \textbf{x}_{n+1})
If the expanded point is better than the reflected point, f(\textbf{x}_{e}) < f(\textbf{x}_{r})
then obtain a new simplex by replacing the worst point \textbf{x}_{n+1} with the expanded point \textbf{x}_{e}, and go to step 1.
Else obtain a new simplex by replacing the worst point \textbf{x}_{n+1} with the reflected point \textbf{x}_{r}, and go to step 1.
Else (i.e. reflected point is not better than second worst) continue at step 5.
  • 5. Contraction
Here, it is certain that f(\textbf{x}_{r}) \geq f(\textbf{x}_{n})
Compute contracted point  \textbf{x}_{c} = \textbf{x}_o+\rho(\textbf{x}_{o}-\textbf{x}_{n+1})
If the contracted point is better than the worst point, i.e. f(\textbf{x}_{c}) < f(\textbf{x}_{n+1})
then obtain a new simplex by replacing the worst point \textbf{x}_{n+1} with the contracted point \textbf{x}_{c}, and go to step 1.
Else go to step 6.
  • 6. Reduction
For all but the best point, replace the point with
\textbf{x}_{i} = \textbf{x}_{1} + \sigma(\textbf{x}_{i} - \textbf{x}_{1}) \text{ for all i } \in\{2,\dots,n+1\}. go to step 1.

Note: \alpha, \gamma, \rho and \sigma are respectively the reflection, the expansion, the contraction and the shrink coefficient. Standard values are \alpha =1, \gamma =2, \rho =-1/2 and \sigma =1/2.

For the reflection, since \textbf{x}_{n+1} is the vertex with the higher associated value among the vertices, we can expect to find a lower value at the reflection of \textbf{x}_{n+1} in the opposite face formed by all vertices point \textbf{x}_{i} except \textbf{x}_{n+1}.

For the expansion, if the reflection point \textbf{x}_{r} is the new minimum along the vertices we can expect to find interesting values along the direction from \textbf{x}_{o} to \textbf{x}_{r}.

Concerning the contraction: If f(\textbf{x}_{r}) > f(\textbf{x}_{n}) we can expect that a better value will be inside the simplex formed by all the vertices \textbf{x}_{i}.

Finally, the reduction handles the rare case that contracting away from the largest point increases f, something that cannot happen sufficiently close to a non-singular minimum. In that case we contract towards the lowest point in the expectation of finding a simpler landscape.

The initial simplex is important, indeed, a too small initial simplex can lead to a local search, consequently the NM can get more easily stuck. So this simplex should depend on the nature of the problem.

See also[edit]


  1. ^ a b
  2. ^ a b
    • Yu, Wen Ci. 1979. “Positive basis and a class of direct search techniques”. Scientia Sinica [Zhongguo Kexue]: 53—68.
    • Yu, Wen Ci. 1979. “The convergent property of the simplex evolutionary technique”. Scientia Sinica [Zhongguo Kexue]: 69–77.
    • Kolda, Tamara G.; Lewis, Robert Michael; Torczon, Virginia (2003). "Optimization by direct search: new perspectives on some classical and modern methods". SIAM Rev. 45: 385–482. doi:10.1137/S003614450242889. 
    • Lewis, Robert Michael; Shepherd, Anne; Torczon, Virginia (2007). "Implementing generating set search methods for linearly constrained minimization". SIAM J. Sci. Comput. 29: 2507–2530. doi:10.1137/050635432. 
  3. ^ Nelder, John A.; R. Mead (1965). "A simplex method for function minimization". Computer Journal 7: 308–313. doi:10.1093/comjnl/7.4.308. 

Further reading[edit]

  • Avriel, Mordecai (2003). Nonlinear Programming: Analysis and Methods. Dover Publishing. ISBN 0-486-43227-0.
  • Coope, I. D.; C.J. Price, 2002. “Positive bases in numerical optimization”, Computational Optimization & Applications, Vol. 21, No. 2, pp. 169–176, 2002.
  • Press, WH; Teukolsky, SA; Vetterling, WT; Flannery, BP (2007). "Section 10.5. Downhill Simplex Method in Multidimensions". Numerical Recipes: The Art of Scientific Computing (3rd ed.). New York: Cambridge University Press. ISBN 978-0-521-88068-8. 

External links[edit]