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    <p>As Peter says, you have to minimize a function <code>f(a,b,c)</code>. There are a lot of elaborate methods for well behaving functions. Eg for functions which can be differentiated, or for so called convex functions. In your case you have a function where we do not know very much about. So f could have different local minima which kills many established minimization methods.</p> <p>If a simple evaluation of a parameter set <code>a,b,c</code> is fast, you can try some kind of coordinate descent. This is not the best method, brute force but easy for you to implement. I will name the standard deviation achieved by <code>(a,b,c)</code> as <code>s(a,b,c)</code>:</p> <p>I give you some python style pseudo code, which should be easy to read:</p> <pre><code> def improve(a,b,c): eps = .01 s1 = s(a*(1+eps), b, c) s2 = s(a, b*(1+eps), b, c) s3 = s(a, b, c*(1+eps)) s4 = s(a*(1-eps), b, c) s5 = s(a, b*(1-eps), c) s6 = s(a, b, c*(1-eps)) # determine minimal of (s1....s6) and take index: i = argmin (s1....s6) # take parameters which lead to miminal si: if i==1: a = a*(1+eps) if i==2: b = b*(1+eps) ... if i==6: c = c*(1-eps) return a,b ,c </code></pre> <p>You have to start with some values <code>(a,b,c)</code> and this function should give you a new triple <code>(a,b,c)</code> which leads to less variation. Now you can apply this step as often as you want. Maybe you have to adapt <code>eps</code>, that depends on how fast <code>s(a,b,c)</code> changes if you make little modifications on <code>a</code>, <code>b</code>, or <code>c</code>.</p> <p>This is not the best solution, but an easy to try hands-on approach.</p>
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