This problem can be easily equated as a minimization problem. It's unique solution depends on convexity. Giving three functions $f(x,y)=0,g(x,y)=0,h(x,y)=0$ and calling
$$
\cases{
\vec n_f=\frac{\nabla f}{||\nabla f||}\\
\vec n_g=\frac{\nabla g}{||\nabla g||}\\
\vec n_h=\frac{\nabla h}{||\nabla h||}\\
p_f = \{x_f,y_f\}\ |\ f(x_f,y_f)=0\\
p_g = \{x_g,y_g\}\ |\ g(x_g,y_g)=0\\
p_h = \{x_h,y_h\}\ |\ h(x_h,y_h)=0\\
o_f = p_f+\lambda_f\vec n_f\\
o_g = p_g+\lambda_g\vec n_g\\
o_h = p_h+\lambda_h\vec n_h\\
}
$$
the minimization problem can be formulated as
$$
\min_{p_f,p_g,p_h,\lambda_f,\lambda_g,\lambda_h}||o_f-o_g||^2+||o_g-o_h||^2+||o_f-o_h||^2\ \ \text{s. t.}\ \ \ |\lambda_f|=|\lambda_g|=|\lambda_h|
$$
Follows a MATHEMATICA script for
$$
\cases{
f(x,y) = x^x-y=0\\
g(x,y) = \ln x^{\ln x}-y=0\\
h(x,y) = \left(\ln x\right)^2-y=0
}
$$
f[x_] := x^x
g[x_] := Log[x]^Log[x]
h[x_] := Log[x]^2
pf = {x1, f[x1]};
pg = {x2, g[x2]};
ph = {x3, h[x3]};
nf = Grad[y1 - f[x1], {x1, y1}];
ng = Grad[y2 - g[x2], {x2, y2}];
nh = Grad[y3 - h[x3], {x3, y3}];
of = pf + lambda1 nf/Norm[nf];
og = pg + lambda2 ng/Norm[ng];
oh = ph + lambda3 nh/Norm[nh];
obj = Norm[of - og] + Norm[of - oh] + Norm[og - oh];
sol = NMinimize[{obj, 1.5 > x1 > 0.5, 1.5 > x2 > 1, 1.5 > x3 > 0.5, lambda1^2 == lambda2^2 == lambda3^2}, {lambda1, lambda2, lambda3, x1, x2, x3}, Method -> "DifferentialEvolution"]
plot = Plot[{f[x], g[x], h[x]}, {x, 0, E}, PlotRange -> {0, 1}];
{x0, y0} = of /. sol[[2]]
r = lambda1 /. sol[[2]]
circ = (x - x0)^2 + (y - y0)^2 - r^2;
grc = ContourPlot[circ == 0, {x, 0, 2}, {y, 0, 1}];
Show[plot, grc, AspectRatio -> 1/3]
![enter image description here](https://cdn.statically.io/img/i.sstatic.net/mfiCi.jpg)
NOTE
Additional restrictions were included to avoid calculations involving complex numbers.
Another example (a little more general) with
$$
\cases{
f(x,y) = y-x-1.25=0\\
g(x,y) = x^2+y^2-9=0\\
h(h,y) = x^2+y = 0
}
$$
Follows the new script
Clear[f, g, h]
f[x_, y_] := y - x - 1.25
g[x_, y_] := x^2 + y^2 - 9
h[x_, y_] := x^2 + y
pf = {x1, y1};
pg = {x2, y2};
ph = {x3, y3};
nf = Grad[f[x1, y1], {x1, y1}];
ng = Grad[g[x2, y2], {x2, y2}];
nh = Grad[h[x3, y3], {x3, y3}];
of = pf + lambda1 nf/Norm[nf];
og = pg + lambda2 ng/Norm[ng];
oh = ph + lambda3 nh/Norm[nh];
obj = Norm[of - og]^2 + Norm[of - oh]^2 + Norm[og - oh]^2;
sol1 = NMinimize[{obj, f[x1, y1] == g[x2, y2] == h[x3, y3] == 0, Abs[lambda1] == Abs[lambda2] == Abs[lambda3], -3 < x1 < 0, -3 < x2 < 0, -3 < x3 < 0, -3 < y1 < 0, -3 < y2 < 0, -3 < y3 < 0}, {lambda1, lambda2, lambda3, x1, x2, x3, y1, y2, y3}]
sol2 = NMinimize[{obj, f[x1, y1] == g[x2, y2] == h[x3, y3] == 0, Abs[lambda1] == Abs[lambda2] == Abs[lambda3], 0 < x1 < 3, 0 < x2 < 3, 0 < x3 < 3, -0.3 < y1 < 2, -0.3 < y2 < 2, -0.3 < y3 < 2}, {lambda1, lambda2, lambda3, x1, x2, x3, y1, y2, y3}, Method -> "DifferentialEvolution"]
plot = ContourPlot[{f[x, y] == 0, g[x, y ] == 0, h[x, y] == 0}, {x, -3, 3}, {y, -3, 3}];
{x0, y0} = of /. sol1[[2]]
r = Norm[of - pf] /. sol1[[2]]
circ = (x - x0)^2 + (y - y0)^2 - r^2;
grc1 = ContourPlot[circ == 0, {x, -3, 3}, {y, -3, 3}];
{x0, y0} = of /. sol2[[2]]
r = Norm[of - pf] /. sol2[[2]]
circ = (x - x0)^2 + (y - y0)^2 - r^2;
grc2 = ContourPlot[circ == 0, {x, -3, 3}, {y, -3, 3}];
Show[plot, grc1, grc2]
![enter image description here](https://cdn.statically.io/img/i.sstatic.net/Q9O4i.jpg)