Junyu Zhang

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We address the problem of autonomously exploring unknown objects in a scene by consecutive depth acquisitions. The goal is to reconstruct the scene while online identifying the objects from among a large collection of 3D shapes. Fine-grained shape identification demands a meticulous series of observations attending to varying views and parts of the object(More)
Recent research indicates that Markov decision processes (MDPs) and perturbation analysis (PA) based optimization can be derived easily from two fundamental performance sensitivity formulas. With this sensitivity point of view, an event-based optimization approach, including event-based sensitivity analysis and event-based policy iteration, was proposed via(More)
The paper proposes a new approach to the theory of Markov decision processes (MDPs) with average performance criteria and finite state and action spaces. Using the average performance and bias difference formulas derived in this paper, we develop an optimization theory for average performance (or gain) optimality, bias optimality, and all the high-order(More)
This paper deals with the bias optimality ofmultichainmodels for finite continuous-timeMarkov decision processes. Based on new performance difference formulas developed here, we prove the convergence of a so-called bias-optimal policy iteration algorithm, which can be used to obtain bias-optimal policies in a finite number of iterations. © 2009 Elsevier(More)
Computing a few eigenpairs from large-scale symmetric eigenvalue problems is far beyond the tractability of classic eigensolvers when the storage of the eigenvectors in the classical way is impossible. We consider a tractable case in which both the coefficient matrix and its eigenvectors can be represented in the low-rank tensor train formats. We propose a(More)
Abstract. In this paper, we consider the community detection problem under either the stochastic block model (SBM) assumption or the degree-correlated stochastic block model (DCSBM) assumption. The modularity maximization formulation for the community detection problem is NP-hard in general. In this paper, we propose a sparse and low-rank completely(More)
In this paper, we study the nth-bias optimality problem for finite continuous-time Markov decision processes (MDPs) with a multichain structure. We first provide nth-bias difference formulas for two policies and present some interesting characterizations of an nth-bias optimal policy by using these difference formulas. Then, we prove the existence of an(More)
This paper deals with the approximation problem of the first passage models for discrete-time Markov decision processes (MDPs) with varying discount factors. For a given control model M, by using a finite-state and finite-action truncation technique, we show that the first passage optimal reward and policies of M can be approximated by those of the solvable(More)