Non-Negative Orthogonal Greedy Algorithms

Greedy metrics in orthogonal greedy learning

Orthogonal greedy learning (OGL) is a stepwise learning scheme that adds a new atom from a dictionary via the steepest gradient descent and build the estimator via orthogonal projecting the target function to the space spanned by the selected atoms in each greedy step. Here, “greed” means choosing a new atom according to the steepest gradient descent principle. OGL then avoids the overfitting/u...

1 Self Orthogonal Greedy Codes

In this paper, we examine greedily generated self orthogonal codes. We show that they are linear, and discuss several properties which they possess. We also look at specific examples of these codes.

Greedy Algorithms

Greedy Algorithms for High-dimensional Non-symmetric Linear Problems

In this article, we present a family of numerical approaches to solve high-dimensional linear non-symmetric problems. The principle of these methods is to approximate a function which depends on a large number of variates by a sum of tensor product functions, each term of which is iteratively computed via a greedy algorithm [20]. There exists a good theoretical framework for these methods in th...

Greedy Algorithms: Huffman Coding

Greedy Algorithms: In an optimization problem, we are given an input and asked to compute a structure, subject to various constraints, in a manner that either minimizes cost or maximizes profit. Such problems arise in many applications of science and engineering. Given an optimization problem, we are often faced with the question of whether the problem can be solved efficiently (as opposed to a...