Calibrated $α$-Barycenters for Multiobjective $f$-Divergence Optimization
Matching one distribution to many targets can require repeated evaluations of every target. CAL-$α$-MODM aggregates a nonlinear predictor into one barycenter, calibrates its residual derivatives, and checks steps against the original $f$-divergence objective. Under smoothness assumptions, calibration controls parameter-step model error without requiring targets to be close to the model. Exact-oracle stationarity and local convergence guarantees are established. At common matching, canonical geometry cancels the leading quadratic iteration error, giving cubic convergence with second-order calibration and sufficiently accurate inner solves. High-precision experiments support this prediction...
| Authors | Dier Tang, Peijie Li, Xiaoyuan Zhang |
|---|---|
| Primary category | math.OC |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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