6 accepted papers
Distributed optimization is widely used to solve large-scale optimization problems by parallelizing gradient-based algorithms across multiple computing nodes. In asynchronous optimization, the optimization parameter is updated using stale gradients, which are gradients calculated with respect to out…
Elasticity is offered by cloud service providers to exploit under-utilized computing resources. The low-cost elastic nodes can leave and join any time during the computation cycle. The possibility of elastic events occurring together with the problem of slow nodes, referred to as stragglers, increas…
We present convergence analysis of Anytime Minibatch with Delayed Gradients (AMB-DG) algorithm. In AMB-DG, workers compute gradients in epochs of fixed duration while the master uses stale gradients to update the optimization parameters. We analyze AMB-DG in terms of its regret bound and convergence…
We consider decentralized consensus optimization when workers sample data from non-identical distributions and perform variable amounts of work due to slow nodes known as stragglers. The problem of non-identical distributions and the problem of variable amount of work have been previously studied se…
Gradient-based algorithms play an important role in solving a wide range of stochastic optimization problems. In recent years, implementing such schemes in parallel has become the new paradigm. In this work, we focus on the asynchronous implementation of gradient-based algorithms. In asynchronous di…
We detail a field-programmable gate array (FPGA) based implementation of linear programming (LP) decoding. LP decoding frames error correction as an optimization problem. This is in contrast to variants of belief propagation (BP) that view error correction as a problem of graphical inference. LP dec…