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  1. Abstract With the motivation to study how non-magnetic ion site disorder affects the quantum magnetism of Ba 3 CoSb 2 O 9 , a spin-1/2 equilateral triangular lattice antiferromagnet, we performed DC and AC susceptibility, specific heat, elastic and inelastic neutron scattering measurements on single crystalline samples of Ba 2.87 Sr 0.13 CoSb 2 O 9 with Sr doping on non-magnetic Ba 2+ ion sites. The results show that Ba 2.87 Sr 0.13 CoSb 2 O 9 exhibits (i) a two-step magnetic transition at 2.7 K and 3.3 K, respectively; (ii) a possible canted 120 degree spin structure at zero field withmore »reduced ordered moment as 1.24 μ B /Co; (iii) a series of spin state transitions for both H ∥ ab -plane and H ∥ c -axis. For H ∥ ab -plane, the magnetization plateau feature related to the up–up–down phase is significantly suppressed; (iv) an inelastic neutron scattering spectrum with only one gapped mode at zero field, which splits to one gapless and one gapped mode at 9 T. All these features are distinctly different from those observed for the parent compound Ba 3 CoSb 2 O 9 , which demonstrates that the non-magnetic ion site disorder (the Sr doping) plays a complex role on the magnetic properties beyond the conventionally expected randomization of the exchange interactions. We propose the additional effects including the enhancement of quantum spin fluctuations and introduction of a possible spatial anisotropy through the local structural distortions.« less
    Free, publicly-accessible full text available March 10, 2023
  2. In an optimal design problem, we are given a set of linear experiments v1,…,vn∈Rd and k≥d, and our goal is to select a set or a multiset S⊆[n] of size k such that Φ((∑i∈Sviv⊤i)−1) is minimized. When Φ(M)=Determinant(M)1/d, the problem is known as the D-optimal design problem, and when Φ(M)=Trace(M), it is known as the A-optimal design problem. One of the most common heuristics used in practice to solve these problems is the local search heuristic, also known as the Fedorov’s exchange method (Fedorov, 1972). This is due to its simplicity and its empirical performance (Cook and Nachtrheim, 1980; Millermore »and Nguyen, 1994; Atkinson et al., 2007). However, despite its wide usage no theoretical bound has been proven for this algorithm. In this paper, we bridge this gap and prove approximation guarantees for the local search algorithms for D-optimal design and A-optimal design problems. We show that the local search algorithms are asymptotically optimal when kd is large. In addition to this, we also prove similar approximation guarantees for the greedy algorithms for D-optimal design and A-optimal design problems when k/d is large.« less
  3. In an optimal design problem, we are given a set of linear experiments v1,…,vn∈Rd and k≥d, and our goal is to select a set or a multiset S⊆[n] of size k such that Φ((∑i∈Sviv⊤i)−1) is minimized. When Φ(M)=Determinant(M)1/d, the problem is known as the D-optimal design problem, and when Φ(M)=Trace(M), it is known as the A-optimal design problem. One of the most common heuristics used in practice to solve these problems is the local search heuristic, also known as the Fedorov’s exchange method (Fedorov, 1972). This is due to its simplicity and its empirical performance (Cook and Nachtrheim, 1980; Millermore »and Nguyen, 1994; Atkinson et al., 2007). However, despite its wide usage no theoretical bound has been proven for this algorithm. In this paper, we bridge this gap and prove approximation guarantees for the local search algorithms for D-optimal design and A-optimal design problems. We show that the local search algorithms are asymptotically optimal when kd is large. In addition to this, we also prove similar approximation guarantees for the greedy algorithms for D-optimal design and A-optimal design problems when kd is large.« less
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