Market conditions change continuously. However, in portfolio investment strategies, it is hard to account for this intrinsic non-stationarity. In paper, we propose address issue by using the Inverse Covariance Clustering (ICC) method identify inherent market states and then integrate such into a dynamic optimization process. Extensive experiments across three different markets, NASDAQ, FTSE HS3...

Solving the inverse kinematics problem is a fundamental challenge in motion planning, control, and calibration for articulated robots. Kinematic models these robots are typically parametrized by joint angles, generating complicated mapping between robot configuration end-effector pose. Alternatively, kinematic model task constraints can be represented using invariant distances points attached t...

We introduce a new class of inverse optimization problems in which an input solution is given together with k linear weight functions, and the goal to modify weights by same deviation vector p so that becomes optimal respect each them, while minimizing ‖p‖1. In particular, we concentrate on three multiple functions: shortest s−t path, bipartite perfect matching, arborescence problems. Using LP ...

Real-time control of the endeffector of a humanoid robot in external coordinates requires computationally efficient solutions of the inverse kinematics problem. In this context, this paper investigates inverse kinematics learning for resolved motion rate control (RMRC) employing an optimization criterion to resolve kinematic redundancies. Our learning approach is based on the key observations t...

Consider a problem where set of feasible observations are provided by an expert and cost function is defined that characterizes which the dominate others hence, preferred. Our goal to find linear constraints would render all given while making preferred ones optimal for (objective) function. By doing so, we infer implicit region programming problem. Providing such regions (i) builds baseline ca...