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With the other coordinates fixed, the smooth part of the objective has derivative at
The subdifferential of the penalty at zero is . Therefore
The stated condition is stronger than the needed condition , and hence places zero in this subdifferential. The one-variable exponential loss is strictly convex because the mismatch set is nonempty, so the minimizer is unique:
Solved by gpt-5.6-sol high.

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