10 papers
Online Inverse Integer Linear Optimization via Small-Gradient Skipping: Constant Regret and Finite Mistakes
Akira Kitaoka
In online inverse linear optimization, the learner predicts a weight at each round, observes the optimal action of the agent, and updates its prediction. In the general setting, th…
Explicit Iteration Complexity of Exact Data-Driven Inverse Optimization for Integer Linear Programs
Akira Kitaoka
A data-driven inverse optimization problem (DDIOP) is the problem of estimating the objective-function parameters (weights) that explain observed optimal-solution data, and it aris…
Inverse Mixed-Integer Programming: Learning Constraints then Objective Functions
Akira Kitaoka
Data-driven inverse optimization for mixed-integer linear programs (MILPs), which seeks to learn an objective function and constraints consistent with observed decisions, is import…
Minimization of curve length through energy minimization using finite differences and numerical integration in Euclidean space
Akira Kitaoka
We consider the approximation of minimal geodesics between two closed sets in endowed with a smooth Riemannian metric. The continuous problem is formulated as the mi…
Exact Solution to Data-Driven Inverse Optimization of MILPs in Finite Time via Gradient-Based Methods
Akira Kitaoka
A data-driven inverse optimization problem (DDIOP) is the problem of estimating the objective-function parameters (weights) that explain observed optimal-solution data, and it aris…
A proof of imitation of Wasserstein inverse reinforcement learning for multi-objective optimization
Akira Kitaoka, Riki Eto
We prove Wasserstein inverse reinforcement learning enables the learner's reward values to imitate the expert's reward values in a finite iteration for multi-objective optimization…