In this paper, we study inverse optimization problems defined as follows: Let S denote the set of feasible solutions of an optimization problem P, let c be a specified cost vector, and x0 be a given feasible solution. The solution x0 may or may not be an optimal solution of P with respect to the cost vector c. The inverse optimization problem is to perturb the cost vector c to d so that x0 is a...

one of the most important goals in geomagnetic investigations is detecting local anomaly locations. regional anomaly can be simulating as a trend surface, and local anomalies will be detected by comparison of measured data and simulated trend surface. the problem is trying to find best coefficients of trend surface model using inverse methods based on modern optimization techniques, which are f...

Although inverse linear programming (LP) has received increasing attention as a technique to identify an LP that can reproduce observed decisions are originally from complex system, the performance of objective function inferred by existing methods is often highly sensitive noise, errors, and uncertainty in underlying decision data. Inspired robust regression techniques mitigate impact noisy da...

The past decade has seen a growing interest in inverse optimization. It has been shown that duality yields very efficient algorithms for solving inverse linear programming problems. In this paper, we consider a special class of conic programs that admits a similar duality and show that the corresponding inverse optimization problems are efficiently solvable. We discuss the applications of inver...

this paper proposes a new approach for determining efficient dmus in dea models using inverse optimi-zation and without solving any lps. it is shown that how a two-phase algorithm can be applied to detect effi-cient dmus. it is important to compare computational performance of solving the simultaneous linear equa-tions with that of the lp, when computational issues and complexity analysis are a...