  We propose a delay-agnostic asynchronous coordinate update algorithm (DEGAS)
for computing operator fixed points, with applications to asynchronous
optimization. DEGAS includes novel asynchronous variants of ADMM and
block-coordinate descent as special cases. We prove that DEGAS converges under
both bounded and unbounded delays under delay-free parameter conditions. We
also validate by theory and experiments that DEGAS adapts well to the actual
delays. The effectiveness of DEGAS is demonstrated by numerical experiments on
classification problems.
