A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Method
In this paper, we propose a trust-region interior-point stochastic sequential quadratic programming (TR-IP-SSQP) method for solving optimization problems with a stochastic objective and deterministic nonlinear equality and inequality constraints. In this setting, exact evaluations of the objective function and its gradient are unavailable, but their stochastic estimates can be constructed. In particular, at each iteration our method builds stochastic oracles, which estimate the objective value and gradient to satisfy proper adaptive accuracy conditions with a fixed probability. To handle inequality constraints, we adopt an interior-point method (IPM), in which the barrier parameter follow...
| Authors | Yuchen Fang, Jihun Kim, Sen Na et al. |
|---|---|
| Primary category | math.OC |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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