3,131 research outputs found
Phase diagram of Kondo-Heisenberg model on honeycomb lattice with geometrical frustration
We calculated the phase diagram of the Kondo-Heisenberg model on
two-dimensional honeycomb lattice with both nearest-neighbor and
next-nearest-neighbor antiferromagnetic spin exchanges, to investigate the
interplay between RKKY and Kondo interactions at presence of magnetic
frustration. Within a mean-field decoupling technology in slave-fermion
representation, we derived the zero-temperature phase diagram as a function of
Kondo coupling and frustration strength . The geometrical frustration
can destroy the magnetic order, driving the original antiferromagnetic (AF)
phase to non-magnetic valence bond state (VBS). In addition, we found two
distinct VBS. As is increased, a phase transition from AF to Kondo
paramagnetic (KP) phase occurs, without the intermediate phase coexisting AF
order with Kondo screening found in square lattice systems. In the KP phase,
the enhancement of frustration weakens the Kondo screening effect, resulting in
a phase transition from KP to VBS. We also found a process to recover the AF
order from VBS by increasing in a wide range of frustration strength. Our
work may provide deeper understanding for the phase transitions in
heavy-fermion materials, particularly for those exhibiting triangular
frustration
On Weighted Graph Sparsification by Linear Sketching
A seminal work of [Ahn-Guha-McGregor, PODS'12] showed that one can compute a
cut sparsifier of an unweighted undirected graph by taking a near-linear number
of linear measurements on the graph. Subsequent works also studied computing
other graph sparsifiers using linear sketching, and obtained near-linear upper
bounds for spectral sparsifiers [Kapralov-Lee-Musco-Musco-Sidford, FOCS'14] and
first non-trivial upper bounds for spanners [Filtser-Kapralov-Nouri, SODA'21].
All these linear sketching algorithms, however, only work on unweighted graphs.
In this paper, we initiate the study of weighted graph sparsification by
linear sketching by investigating a natural class of linear sketches that we
call incidence sketches, in which each measurement is a linear combination of
the weights of edges incident on a single vertex. Our results are:
1. Weighted cut sparsification: We give an algorithm that computes a -cut sparsifier using linear
measurements, which is nearly optimal.
2. Weighted spectral sparsification: We give an algorithm that computes a -spectral sparsifier using
linear measurements. Complementing our algorithm, we then prove a superlinear
lower bound of measurements for computing some
-spectral sparsifier using incidence sketches.
3. Weighted spanner computation: We focus on graphs whose largest/smallest
edge weights differ by an factor, and prove that, for incidence
sketches, the upper bounds obtained by~[Filtser-Kapralov-Nouri, SODA'21] are
optimal up to an factor
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