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Creators/Authors contains: "Laddha, Aditi"

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  1. ; ; (, Operations Research Letters)
    In the minimum eigenvalue problem, we are given a collection of vectors and the goal is to pick a subset B to maximize the minimum eigenvalue of the matrix formed by the sum of their outer products. We give a -time randomized algorithm that finds an assignment subject to a partition constraint whose minimum eigenvalue is at least $$1-\epsilon$$ times the optimum, with high probability. As a byproduct, we also get a simple algorithm for an algorithmic version of Kadison-Singer problem. 
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    Free, publicly-accessible full text available November 1, 2025
  2. ; ; ; (, Society for Industrial and Applied Mathematics)
    Full Text Available 
  3. ; ; (, Operations Research Letters)
    Full Text Available 
  4. ; ; ; ; (, 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS))
    Full Text Available 
  5. ; ; (, APPROX/RANDOM.2022)
    Accepted Manuscript Full Text Available
  6. ; ; (, RANDOM-APPROX)
    We study a unified approach and algorithm for constructive discrepancy minimization based on a stochastic process. By varying the parameters of the process, one can recover various state-of-the-art results. We demonstrate the flexibility of the method by deriving a discrepancy bound for smoothed instances, which interpolates between known bounds for worst-case and random instances. 
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    Accepted Manuscript Full Text Available
  7. ; ; ; (, 53rd Annual ACM SIGACT Symposium on Theory of Computing)
    null (Ed.)
    Full Text Available 
  8. ; (, ACM Symposium on Computational Geometry)
    Buchin, Kevin; Colin de Verdi\` (Ed.)
    The Gibbs Sampler is a general method for sampling high-dimensional distributions, dating back to 1971. In each step of the Gibbs Sampler, we pick a random coordinate and re-sample that coordinate from the distribution induced by fixing all the other coordinates. While it has become widely used over the past half-century, guarantees of efficient convergence have been elusive. We show that for a convex body K in ℝⁿ with diameter D, the mixing time of the Coordinate Hit-and-Run (CHAR) algorithm on K is polynomial in n and D. We also give a lower bound on the mixing rate of CHAR, showing that it is strictly worse than hit-and-run and the ball walk in the worst case. 
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    Full Text Available 
  9. ; ; (, ACM Symposium on the Theory of Computing)
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