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How to effectively find a starting point solving a non-linear equation?

Computational Science Asked by colombien on November 24, 2020

I have the following equation (the Kurz-Giovanola-Trivedi model [1])
$$
v^2 frac{pi^2 Gamma}{P^2 D^2} + v frac{mC_0(1-k)xi}{D[1-(1-k)Iv(P)]} + G = 0,
$$

where $Iv(P)=P cdot exp(P) cdot E(P)$, $P=frac{Rv}{2D}$, $E(P)=int_{P}^{infty} frac{exp(-x)}{x} dx$ is the exponential integral function, $xi=1-frac{2k}{[1+(frac{2pi}{P})^2]^{frac{1}{2}}-1+2k}$, $v$ and $R$ are unknowns, and everything else is constant.
To solve this equation, I use MATLAB (the core function is fzero) and suppose that $v$ is an array within a range $(10^{-2};10^2)$. The fzero function attempts to find a zero of one equation with one variable and might be called with either a one-element starting point or a two-element vector (starting interval).

My question is: how to effectively find a starting point?

I have started by solving this equation relative to $P$, and it was relatively easy to determine starting points $P_0$ for different intervals of the $v$ array by trial-and-error method. After finding $P$, it was easy to find $R$.

However, now I need to solve the equation relative to $R$ directly, without the $P$-step. The trial-and-error method is not the best option here, probably due to the type of the $R(v)$-dependence (please see below).

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One Answer

I recommend using a global optimization solver.

You should be able to solve this, or at least try, using the BMIBNB global optimization solver https://yalmip.github.io/solver/bmibnb/ under YALMIP https://yalmip.github.io/tutorial/basics/ under MATLAB.

In particular, note that YALMIP supports the exponential integral, expint, https://yalmip.github.io/command/expint/

For this problem:

Declare the variables v and R as sdpvar.

Enter the equation, and lower and upper bounds on the variables, all inside [] , as the constraints in the 1st argument of the optimize command

Set the objective to [] as the 2nd argument in the optimize command.

Set 'bmibnb' as the solver using sdpsettings as the 3rd argument in the optimize command.

You do not need a starting value. You will need a local nonlinear solver installed under MATLAB which will be called by BMIBNB, as well as a MILP (Mixed Integer Linear Programming) solver to be called by BMIBNB.

Answered by Mark L. Stone on November 24, 2020

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