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  1. Jadamba, B ; Khan, A. A ; Migórski, S ; Sama, M. (Ed.)
    Free, publicly-accessible full text available December 15, 2022
  2. Free, publicly-accessible full text available September 1, 2022
  3. Bender, M. ; Gilbert, J. ; Hendrickson, B. ; Sullivan, B. (Ed.)
    We design new serial and parallel approximation algorithms for computing a maximum weight b-matching in an edge-weighted graph with a submodular objective function. This problem is NP-hard; the new algorithms have approximation ratio 1/3, and are relaxations of the Greedy algorithm that rely only on local information in the graph, making them parallelizable. We have designed and implemented Local Lazy Greedy algorithms for both serial and parallel computers. We have applied the approximate submodular b-matching algorithm to assign tasks to processors in the computation of Fock matrices in quantum chemistry on parallel computers. The assignment seeks to reduce the runmore »time by balancing the computational load on the processors and bounding the number of messages that each processor sends. We show that the new assignment of tasks to processors provides a four fold speedup over the currently used assignment in the NWChemEx software on 8000 processors on the Summit supercomputer at Oak Ridge National Lab.« less
    Free, publicly-accessible full text available August 1, 2022
  4. This work is dedicated to developing an abstract framework for parameter estimation in elliptic variational inequalities. Differentiability of the parameter to solution map in the optimization problems are studied using a smoothing of the penalty map. We derived necessary optimality conditions for the optimization problems under consideration. The feasibility of the approach is tested through numerical experiments.
  5. Inverse problems of identifying parameters in partial differential equations (PDEs) is an important class of problems with many real-world applications. Inverse problems are commonly studied in optimization setting with various known approaches having their advantages and disadvantages. Although a non-convex output least-squares (OLS) objective has often been used, a convex modified output least-squares (MOLS) attracted quite an attention in recent years. However, the convexity of the MOLS has only been established for parameters appearing linearly in the PDEs. The primary objective of this work is to introduce and analyze a variant of the MOLS for the inverse problem of identifyingmore »parameters that appear nonlinearly in variational problems. Besides giving an existence result for the inverse problem, we derive the first-order and second-order derivative formulas for the new functional and use them to identify the conditions under which the new functional is convex. We give a discretization scheme for the continuous inverse problem and prove its convergence. We also obtain discrete formulas for the new MOLS functional and present detailed numerical examples.« less
  6. A bstract A search for a heavy resonance decaying into a top quark and a W boson in proton-proton collisions at $$ \sqrt{s} $$ s = 13 TeV is presented. The data analyzed were recorded with the CMS detector at the LHC and correspond to an integrated luminosity of 138 fb − 1 . The top quark is reconstructed as a single jet and the W boson, from its decay into an electron or muon and the corresponding neutrino. A top quark tagging technique based on jet clustering with a variable distance parameter and simultaneous jet grooming is used tomore »identify jets from the collimated top quark decay. The results are interpreted in the context of two benchmark models, where the heavy resonance is either an excited bottom quark b ∗ or a vector-like quark B. A statistical combination with an earlier search by the CMS Collaboration in the all-hadronic final state is performed to place upper cross section limits on these two models. The new analysis extends the lower range of resonance mass probed from 1.4 down to 0.7 TeV. For left-handed, right-handed, and vector-like couplings, b ∗ masses up to 3.0, 3.0, and 3.2 TeV are excluded at 95% confidence level, respectively. The observed upper limits represent the most stringent constraints on the b ∗ model to date.« less
    Free, publicly-accessible full text available April 1, 2023