The syntax is the same as for a system of ordinary differential equations. With the above, I get 14 solutions, but these are not all different up to small tolerances. DSolve can also solve differential-algebraic equations. A number of Indian mathematicians gave rules equivalent to the quadratic formula. 210-290) solved the quadratic equation, but giving only one root, even when both roots were positive (Smith 1951, p. The default in Gurobi is to return just one optimal solution, and since the objective is just 0 here, it returns one feasible point. In his work Arithmetica, the Greek mathematician Diophantus (ca. Where the 1000 in "PoolSolutions" is some arbitrary but large enough number for the number of solutions you expect to capture. t_optimizer_attribute(model, "PoolSolutions", 1000) MAC: model = JuMP.Model(Gurobi.Optimizer)Īdd this after the above: t_optimizer_attribute(model, "PoolSearchMode", 2) #Seems to find only 1 fixed point M1=>40, M2=>0 t_optimizer_attribute(model, "NonConvex", 2) Using JuMP with Gurobi it was only able to identify 1 fixed point while Homotop圜ontinuation was able to find all 3 (unless I have implemented something incorrectly). The system is governed by 14 parameters: kb1 = 0.01 Īnd is solved in the following way in Mathematica: Solve[Īs an aside: I ran a different parameter set that should produce 3 fixed points (2 stable and 1 unstable) as determined by Mathematica. The documentation provided a simple example that I wasn’t able to extrapolate to my Mathematica code (below) successfully. Is anyone able to comment on whether it would be possible to call Mathematica into Julia using MathLink.jl to solve the below system. I’m currently using Mathematica to symbolically solve a system of non-linear ODEs (10 equations with 8 variables) as I believe this is not yet possible using Symbolics.jl.
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