In this paper we propose a variant of the random coordinate descent method for solving linearly constrained convex optimization problems with composite objective functions. If the smooth part of the objective function has Lipschitz continuous gradient, then we prove that our method obtains an ε-optimal solution in O(N/ε) iterations, where N is the number of blocks. For the class of problems wit...

Developmental coordination disorder (DCD) describes a condition of poor motor performance in the absence intellectual impairment. Despite being one most prevalent developmental disorders, little is known about how fundamental visuomotor processes might function this group. One idea children with DCD interact their environment less predictive fashion than typically developing children. A metric ...

Submodular function minimization is a fundamental optimization problem that arises in several applications in machine learning and computer vision. The problem is known to be solvable in polynomial time, but general purpose algorithms have high running times and are unsuitable for large-scale problems. Recent work have used convex optimization techniques to obtain very practical algorithms for ...

The iteration complexity of the block-coordinate descent (BCD) type algorithm has been under extensive investigation. It was recently shown that for convex problems the classical cyclic BCGD (block coordinate gradient descent) achieves an O(1/r) complexity (r is the number of passes of all blocks). However, such bounds are at least linearly depend on K (the number of variable blocks), and are a...