The Hybrid Momentum Stochastic Frank-Wolfe algorithm achieves O(K^{-1/4}) convergence in the generalized Frank-Wolfe gap for non-convex stochastic compositional optimization with Lipschitz outer functions.
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A Jump-HMM-driven modified Heston model generates synthetic implied volatility surfaces and American option prices directly from simulated equity return paths, breaking the circular dependency on market-derived volatility.
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Stochastic Compositional Optimization via Hybrid Momentum Frank--Wolfe
The Hybrid Momentum Stochastic Frank-Wolfe algorithm achieves O(K^{-1/4}) convergence in the generalized Frank-Wolfe gap for non-convex stochastic compositional optimization with Lipschitz outer functions.
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Synthetic American Option Pricing via Jump-HMM-Driven Heston Implied Volatility
A Jump-HMM-driven modified Heston model generates synthetic implied volatility surfaces and American option prices directly from simulated equity return paths, breaking the circular dependency on market-derived volatility.