The rate of convergence of the classical Thresholding Greedy Algorithm with respect to bases is studied in this paper. We bound the error of approximation by the product of both norms -- the norm of $f$ and the $A_1$-norm of $f$. We obtain some results for greedy bases, unconditional bases, and quasi-greedy bases. In particular, we prove that our bounds for the trigonometric basis and for the Haar basis are optimal.
翻译:本文研究经典阈值贪心算法相对于基的收敛速率。我们通过$f$的范数和$f$的$A_1$范数的乘积来限制逼近误差。对于贪心基、无条件基和拟贪心基,我们得到了一些结果。特别地,我们证明了三角形基和哈尔基的限制是最优的。