Weijun Xie

ALSO-X#: Better Convex Approximations for Distributionally Robust Chance Constrained Programs

  • Nan Jiang
  • Weijun Xie
    • Categories Stochastic Programming Tags , , ,

    This paper studies distributionally robust chance constrained programs (DRCCPs), where the uncertain constraints must be satisfied with at least a probability of a prespecified threshold for all probability distributions from the Wasserstein ambiguity set. As DRCCPs are often nonconvex and challenging to solve optimally, researchers have been developing various convex inner approximations. Recently, ALSO-X has … Read more

    Fair and Risk-averse Urban Air Mobility Resource Allocation Under Uncertainties

  • Luying Sun
  • Weijun Xie
    • Categories Applications - OR and Management Sciences Tags , , , ,

    Urban Air Mobility (UAM) is an emerging air transportation mode to alleviate the ground traffic burden and achieve zero direct aviation emissions. Due to the potential economic scaling effects, the UAM traffic flow is expected to increase dramatically once implemented, and its market can be substantially large. To be prepared for the era of UAM, … Read more

    On the Exactness of Dantzig-Wolfe Relaxation for Rank Constrained Optimization Problems

  • Yongchun Li
  • Weijun Xie
    • Categories Integer Programming Tags , , , , , ,

    In the rank-constrained optimization problem (RCOP), it minimizes a linear objective function over a prespecified closed rank-constrained domain set and $m$ generic two-sided linear matrix inequalities. Motivated by the Dantzig-Wolfe (DW) decomposition, a popular approach of solving many nonconvex optimization problems, we investigate the strength of DW relaxation (DWR) of the RCOP, which admits the … Read more

    D-optimal Data Fusion: Exact and Approximation Algorithms

  • Yongchun Li
  • Marcia Fampa
  • Jon Lee
  • Feng Qiu
  • Weijun Xie
    • Categories Combinatorial Optimization, Integer Programming Tags , , , , , , ,

    We study the D-optimal Data Fusion (DDF) problem, which aims to select new data points, given an existing Fisher information matrix, so as to maximize the logarithm of the determinant of the overall Fisher information matrix. We show that the DDF problem is NP-hard and has no constant-factor polynomial-time approximation algorithm unless P = NP. … Read more