[Python] Gekko using APOPT isn't optimizing a single linear equation represented as a PWL

I've run into an issue where I can't get APOPT to optimize an unconstrained single piecewise linear, and it's really throwing me for a loop. I feel like there's something I'm not understanding about model.pwl, but it's hard (for me) to find documentation outside of the GEKKO docs. Here's my minimal example:

model = GEKKO(remote=False)
model.options.SOLVER = 1
model.solver_options = ["minlp_as_nlp 0"]

x = model.sos1([0, 1, 2, 3, 4]) # This can also be model.Var(lb=0, ub=4), same result.
pwl = model.Var()

model.pwl(x, pwl, [0, 1, 2, 3, 4], [30, 30.1, 30.2, 30.3, 30.4], bound_x=True)

model.Minimize(pwl)

model.solve(display=True)

print(x.value)
print(pwl.value)
print(model.options.objfcnval)


The output that I get is:

----------------------------------------------------------------
APMonitor, Version 1.0.3
APMonitor Optimization Suite
----------------------------------------------------------------


--------- APM Model Size ------------
Each time step contains
Objects : 1
Constants : 0
Variables : 2
Intermediates: 0
Connections : 2
Equations : 1
Residuals : 1

Piece-wise linear model pwl1points: 5
Number of state variables: 12
Number of total equations: - 5
Number of slack variables: - 0
---------------------------------------
Degrees of freedom : 7

----------------------------------------------
Steady State Optimization with APOPT Solver
----------------------------------------------

Iter Objective Convergence
0 3.39503E+01 3.01000E+01
1 3.22900E+01 1.00000E-10
2 3.22000E+01 2.22045E-16
4 3.22000E+01 0.00000E+00
Successful solution

---------------------------------------------------
Solver : APOPT (v1.0)
Solution time : 3.819999999541324E-002 sec
Objective : 32.2000000000000
Successful solution
---------------------------------------------------


2.0
30.2
32.2


This is unexpected to me, as the obvious minimal value is 30 for the pwl.

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