1) minimum l1 norm solution
极小l1范数解
2) L_1-norm minimizing problems
L1范数极小化问题
3) mini-l 1-norm solution
极小l1模解
4) L 1 norm minimization solution
l1模极小解
5) minimal L1 norm
最小L1范数
1.
A deconvolution approach with an additive regularization term built around an minimal L1 norm is proposed.
文中在基于最小L1范数的加性调整条件下,提出了一种新的去卷积方法。
6) minimum norm solution
极小范数解
1.
According to the characteristic of the inverse problems of ECT, the minimum norm solution was improved using regularization technique, and the stability of the numerical solution was analyzed via singular value decomposition principle.
在分析极小范数解的基础上,针对电容层析成像(ECT)逆问题的特点,利用正则技巧对其进行改进,并利用奇异值分解定理分析了这种改进的数值稳定作用。
2.
Furthermore,the results were applied to the relative inverse eigenvalue problem,and the minimum norm solution for the inverse eigenvalue problem was presented.
同时也把所得结论应用到相应的逆特征值问题,并给出了逆特征值问题的极小范数解。
3.
By further theoretical proof and analysis, it is concluded that at any k th step, the estimated values of the unknowns converge to the minimum norm solution if the equations composed by the preceding k sample data are compatible equations or minimum norm least square solution in case that they are contradictory equations,Moreover, the solut.
本研究对递推最小二乘算法进行了理论证明及分析,指出了在任意第k步,未知参数估计值收敛于前k组数据的极小范数解(如果前k组数据所组成方程组为相容方程组)或者极小范数最小二乘解(如果前k组数据所组成方程组为矛盾方程组),并且此解是唯一的;仿真结果同样也验证了该结论的正确性。
补充资料：Luxemburg范数
Luxemburg范数
Luxemburg nonn
L峨曰血叱范数〔I一血叱~;J如盆c服6yP住肋p-Ma] 函数 ，‘x!.(M，一、{*:*>o，丁、(，一’x(:))‘:‘1}， G这里M(u)是关于正的u递增的偶凸函数， 怒“一’M(u)一忽u(M(u))一，一0，对“>0，M(“)>0，且G是R”中的有界集.此范数的性质曾由W.A.J.h以油比飞〔11作了研究.L~b鸣范数等价于O正ez范数(见0口厄空间(C旧允2 sP创芜))，且 I{x}I(，)簇1 lx}I，蕊2 11 x 11(、).如果函数M(u)和N(u)是互补(或互为对偶)的(见O市口类(Or比zc地”‘、则 ，，·，，(一sun{)·(!，，‘!，“!:，，，，，《一‘，}·如果z‘(t)是可测子集E CG的特征函数，则 !l:二11‘M、-一下尖二一. ““启”‘川M一’(l/n篮‘E)’
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参考词条