This will get you two real values for ‘r_CFV’:
gama = 1.399;
beta = 0.675;
f = @(rcfv) rcfv.^(1-gama)/gama + ((gama-1)/2)*beta.^4 * rcfv.^(2/gama) - (gama+1)/2;
r_cfv(1) = fzero(f, 1)
r_cfv(2) = fzero(f, 10)
x = linspace(-20, 20);
figure(1)
plot(x, f(x))
grid
Use the Optimization Toolbox fsolve function with real and complex initial estimates to get complex values. Another option is the Symbolic Math Toolbox solve function.
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