6 papers
Optimization of Bregman Variational Learning Dynamics
Jinho Cha, Youngchul Kim, Jungmin Shin +3
We develop a general optimization-theoretic framework for Bregman-Variational Learning Dynamics (BVLD), a new class of operator-based updates that unify Bayesian inference, mirror…
Inverse Portfolio Optimization with Synthetic Investor Data: Recovering Risk Preferences under Uncertainty
Jinho Cha, Long Pham, Thi Le Hoa Vo +2
This study develops an inverse portfolio optimization framework for recovering latent investor preferences including risk aversion, transaction cost sensitivity, and ESG orientatio…
Smart Contract-Enabled Procurement under Bounded Demand Variability: A Truncated Normal Approach
Jinho Cha, Youngchul Kim, Junyeol Ryu +3
This study develops a strategic procurement framework integrating blockchain-based smart contracts with bounded demand variability modeled through a truncated normal distribution.…
Smart Contract Adoption in Derivative Markets under Bounded Risk: An Optimization Approach
Jinho Cha, Long Pham, Thi Le Hoa Vo +2
This study develops and analyzes an optimization model of smart contract adoption under bounded risk, linking structural theory with simulation and real-world validation. We examin…
Optimal automation under overdispersed discrete risk: thresholds and hysteresis in a Negative Binomial model
Jinho Cha, Sahng-Min Han, Long Pham +2
Decision-making under clustered uncertainty requires models that accommodate discrete overdispersed risk and endogenous control mechanisms. Standard approaches often impose equi-di…
Dynamic Inverse Optimization under Drift and Shocks: Theory, Regret Bounds, and Applications
JINHO CHA
Operational decisions often reflect priorities that evolve gradually and may shift abruptly after disruptions. We develop a dynamic inverse optimization framework for recovering ti…