direction method. It suffers from high computational cost and is not theoretically guaranteed to converge to a correct solution to the inner loop. In this paper, we propose a novel algorithm to speed up solving TILT, with guaranteed convergence for the inner loop. Our method is based on the recently

and Skids have been used at various times for aircraft landing gear ever since the Wright Flyer appeared in the early 1900's. Typically, skids have been employed as aircraft landing gear either at low speeds or at lowbearing pressures. In this investigation, tests were conducted to examine the friction

several round trips to probe the network for bandwidth. When a transfer is short in length, this leads to poor bandwidth utilization and increased latency, which limit the performance benefits of techniques such as P-HTTP. In this paper, we present a new technique, which we call TCP fast start, to speed

position in k-space can be sampled at a time, making k-space speed the crucial determinant of scan time. Accordingly, gradient performance has been greatly enhanced in the past, reducing minimum scan time drastically with respect to earlier stages of the technique. However, due to both physiological

. The computational results demonstrate that our methods generally outperform the existing methods in terms of solution quality and/or speed. Key words: rank minimization, penalty decomposition methods, matrix completion, nearest lowrank correlation matrix

many others. Our main result is a randomized polynomial time O(1)-speed O(1)-approximation algorithm for this problem. Without speedup, no polynomial time finite multiplicative approximation is possible unless P = NP. We extend the approach of Bansal et al. (FOCS 2007) of rounding a linear programming

High speed scalar processing is an essential characteristic of high performance general purpose computer systems. Highly concurrent execution of scalar code is difficult due to data de-pendencies and conditional branches. This paper proposes an architectural concept called guarded instructions

fragments, and a new form of profile-driven code compression that reduces the speed penalty arising from compression.

In this paper we consider sparse approximation problems, that is, general l0 minimization prob-lems with the l0-“norm ” of a vector being a part of constraints or objective function. In particular, we first study the first-order optimality conditions for these problems. We then propose penalty de

. The proposed model is calibrated using a combination of CORSIM, VISSIM, and SYNCHRO simulation models. Regression analyses are performed to determine the speed penalty imposed on the free flow speed resulting from each of the twelve predictor variables representing geometric, control, and demand conditions