Greedy algorithm

About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.

Minimum padding to satisfy short path constraints

Abstract: Combinational circuits are often embedded in synchronous designs with memory elements at the input and output ports. A performance metric for a circuit is the cycle time of the clock signal. Correct circuit operation requires that all paths have a delay that lies between an upper bound and a lower bound. Traditional approaches in delay optimization for combinational circuits have dealt with methods to decrease the delay of the longest path. We address the issue of satisfying the lower bound constraints. Such a problem also arises in wave pipelining of circuits. We propose to handle short path constraints as a post processing step after traditional delay optimization techniques. There are two issues presented in this paper. We first discuss necessary and sufficient conditions for successful delay insertion without increasing delays of any long paths. In the second part, we present a naive approach to padding delays (greedy heuristic) and an algorithm based on linear programming. We describe an application of the theory to wave pipelining of circuits. Results are presented on a set of benchmark circuits, using two delay models.

Sparse online greedy support vector regression

Abstract: We present a novel algorithm for sparse online greedy kernel-based nonlinear regression. This algorithm improves current approaches to kernel-based regression in two aspects. First, it operates online - at each time step it observes a single new input sample, performs an update and discards it. Second, the solution maintained is extremely sparse. This is achieved by an explicit greedy sparsification process that admits into the kernel representation a new input sample only if its feature space image is linearly independent of the images of previously admitted samples. We show that the algorithm implements a form of gradient ascent and demonstrate its scaling and noise tolerance properties on three benchmark regression problems.

Approximating the two-level facility location problem via a quasi-greedy approach

Abstract: We propose a quasi-greedy algorithm for approximating the classical uncapacitated 2-level facility location problem (2-LFLP). Our algorithm, unlike the standard greedy algorithm, selects a sub-optimal candidate at each step. It also relates the minimization 2-LFLP problem, in an interesting way, to the maximization version of the single level facility location problem. Another feature of our algorithm is that it combines the technique of randomized rounding with that of dual fitting.This new approach enables us to approximate the metric 2-LFLP in polynomial time with a ratio of 1.77, a significant improvement on the previously known approximation ratios. Moreover, our approach results in a local improvement procedure for the 2-LFLP, which is useful in improving the approximation guarantees for several other multi-level facility location problems. An additional result of our approach is an O(ln (n))-approximation algorithm for the non-metric 2-LFLP, where n is the number of clients. This is the first non-trivial approximation for a non-metric multi-level facility location problem.

Forward–Backward Greedy Algorithms for Atomic Norm Regularization

Abstract: In many signal processing applications, the aim is to reconstruct a signal that has a simple representation with respect to a certain basis or frame. Fundamental elements of the basis known as “atoms” allow us to define “atomic norms” that can be used to formulate convex regularizations for the reconstruction problem. Efficient algorithms are available to solve these formulations in certain special cases, but an approach that works well for general atomic norms, both in terms of speed and reconstruction accuracy, remains to be found. This paper describes an optimization algorithm called CoGEnT that produces solutions with succinct atomic representations for reconstruction problems, generally formulated with atomic-norm constraints. CoGEnT combines a greedy selection scheme based on the conditional gradient approach with a backward (or “truncation”) step that exploits the quadratic nature of the objective to reduce the basis size. We establish convergence properties and validate the algorithm via extensive numerical experiments on a suite of signal processing applications. Our algorithm and analysis also allow for inexact forward steps and for occasional enhancements of the current representation to be performed. CoGEnT can outperform the basic conditional gradient method, and indeed many methods that are tailored to specific applications, when the enhancement and truncation steps are defined appropriately. We also introduce several novel applications that are enabled by the atomic-norm framework, including tensor completion, moment problems in signal processing, and graph deconvolution.