# Finding saddle point

**URL:** https://discourse.julialang.org/t/finding-saddle-point/21168
**Category:** General Usage
**Tags:** optim
**Created:** [February 25, 2019, 8:07am UTC](https://discourse.julialang.org/t/finding-saddle-point/21168 "2019-02-25T08:07:02Z")
**Posts on this page:** 1
**Showing post:** 7

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### Author: ![antoine-levitt](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/antoine-levitt/32/4008_2.png) [@antoine-levitt](https://discourse.julialang.org/u/antoine-levitt)
#### Post date: [February 25, 2019, 10:36am UTC](https://discourse.julialang.org/t/finding-saddle-point/21168/7 "2019-02-25T10:36:06Z")

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@cortner note that this problem has an explicit saddle separation: min\_x max\_y f(x,y). That simplifies things a lot, and might actually be a good basis to think about robust algorithms

@amrods essentially yes. But you don’t know how to compute derivatives of F(x) = |nabla f(x)| so you don’t want to do a Newton-like iteration. Also Newton doesn’t see minima, saddles or maxima, it just sees critical points (and so if you start near a minimum it will converge to that). OTOH flipped gradient iteration converges locally to saddles (and not minima or maxima), so anderson acceleration on top of that ([https://github.com/JuliaNLSolvers/NLsolve.jl#anderson-acceleration](https://github.com/JuliaNLSolvers/NLsolve.jl#anderson-acceleration)) may be a good bet (although it will still try to make it converge to any critical point).

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