This note shows that we can recover any complex vector x 0 ∈ C n exactly from on the order of n quadratic equations of the form | a i , x 0 | 2 = b i , i = 1, . . . , m, by using a semidefinite program known as PhaseLift. This improves upon earlier bounds in [3], which required the number of equations to be at least on the order of n log n. Further, we show that exact recovery holds for all input vectors simultaneously, and also demonstrate optimal recovery results from noisy quadratic measurements; these results are much sharper than previously known results.

In this paper we improve existing results in the field of compressed sensing and matrix completion when sampled data may be grossly corrupted. We introduce three new theorems. 1) In compressed sensing, we show that if the m × n sensing matrix has independent Gaussian entries, then one can recover a sparse signal x exactly by tractable ℓ 1 minimization even if a positive fraction of the measurements are arbitrarily corrupted, provided the number of nonzero entries in x is O(m/(log(n/m) + 1)). 2) In the very general sensing model introduced in [7] and assuming a positive fraction of corrupted measurements, exact recovery still holds if the signal now has O(m/(log 2 n)) nonzero entries. 3) Finally, we prove that one can recover an n × n low-rank matrix from m corrupted sampled entries by tractable optimization provided the rank is on the order of O(m/(n log 2 n)); again, this holds when there is a positive fraction of corrupted samples.

A qualitative informational similarity technique has been used to describe the informational orthogonality of projected two-dimensional (2-D) chromatographic separations of complex mixtures from their one-dimensional 1-D separations. The reversed-phase liquid chromatography (RPLC), supercritical fluid chromatography (SFC), gas-liquid chromatography (GLC), and micellar electrokinetic capillary chromatography (MECC) retention behavior of up to 46 solutes of varying molecular properties was studied by 2-D range-scaled retention time plots and information entropy calculations. One hundred five combinations of technique/stationary phase pairs were used to simulate the 2-D chromatographic analyses. The informational entropy of one and two dimensions, the mutual information, the synentropy or "cross information", and the informational similarity were calculated to describe the informational orthogonality. In addition, pattern descriptors were used to qualitatively describe the 2-D peak distribution. With the solutes tested, informational orthogonality, zero informational similarity, was observed with MECC-SDS/SFC-C1, MECC-SDS/SFC-Carbowax, MECC-TTAB/SFC-Carbowax, HPLC-C18/GLC-DB-5, HPLC-PBD/SFC-phenyl, SFC-Carbowax/GLC-DB5, and HPLC-phenyl/SFC-phenyl 2-D chromatographic systems. Conversely, with the solutes tested, informational nonorthogonal behavior described by range-scaled retention time plots to moderate to severe band overlap and data clustering was observed with 2-D chromatographic systems with high informational similarity and moderate to high degrees of synentropy. These results should prove useful for predicting complementary 2-D techniques as well as for choosing a second separation technique for confirmation of separation or peak purity.

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Abstract-We consider the problem of recovering a lowrank matrix when some of its entries, whose locations are not known a priori, are corrupted by errors of arbitrarily large magnitude. It has recently been shown that this problem can be solved efficiently and effectively by a convex program named Principal Component Pursuit (PCP), provided that the fraction of corrupted entries and the rank of the matrix are both sufficiently small. In this paper, we extend that result to show that the same convex program, with a slightly improved weighting parameter, exactly recovers the low-rank matrix even if "almost all" of its entries are arbitrarily corrupted, provided the signs of the errors are random. We corroborate our result with simulations on randomly generated matrices and errors.