  Over the last two decades, a significant line of work in theoretical
algorithms has made progress in solving linear systems whose coefficient matrix
is the Laplacian matrix of a weighted graph. The solution of the linear system
can be interpreted as the potentials of an electrical flow. Kelner, Orrechia,
Sidford, and Zhu (STOC 2013) give a combinatorial, near-linear time algorithm
that maintains the Kirchoff Current Law, and gradually enforces the Kirchoff
Potential Law by updating flows around cycles (cycle toggling). In this paper,
we consider a dual version of the algorithm that maintains the Kirchoff
Potential Law, and gradually enforces the Kirchoff Current Law by cut toggling:
each iteration updates all potentials on one side of a fundamental cut of a
spanning tree by the same amount. We prove that this dual algorithm also runs
in a near-linear number of iterations.
  We show, however, that if we abstract cut toggling as a natural data
structure problem, this problem can be reduced to the online
vector-matrix-vector problem, which has been conjectured to be difficult for
dynamic algorithms by Henzinger, Krinninger, Nanongkai, and Saranurak (STOC
2015). The conjecture implies that a straightforward implementation of the
cut-toggling algorithm requires essentially linear time per iteration. To
circumvent the lower bound, we batch update steps, and perform them
simultaneously instead of sequentially. An appropriate choice of batching leads
to an $\tilde{O}(m^{1.5})$ time cut-toggling algorithm for solving Laplacian
systems. Furthermore, if we sparsify the graph and call our algorithm
recursively on the Laplacian system implied by batching and sparsifying, the
running time can be reduced to $O(m^{1+\epsilon})$ for any $\epsilon > 0$.
Thus, the dual cut-toggling algorithm can achieve (almost) the same running
time as its primal cycle-toggling counterpart.
