Enhanced PDF - Project Euclid
... predicting the eigenvalue distributions of a random matrix plus a deterministic matrix and also of a random matrix multiplied by a deterministic matrix. Relating the sparse case to the nonsparse case in the above theorem is quite useful, since many results are known for random matrices with nonspars ...
... predicting the eigenvalue distributions of a random matrix plus a deterministic matrix and also of a random matrix multiplied by a deterministic matrix. Relating the sparse case to the nonsparse case in the above theorem is quite useful, since many results are known for random matrices with nonspars ...
full version
... T is an instance of Problem 1.1 with signal-to-noise ratio τ, with probability 1 − O(n−10 ), there exists a solution to the degree-4 sum-of-squares relaxation for the MLE problem with objective value at least τ that does not depend on the planted vector v. In particular, no algorithm can reliably re ...
... T is an instance of Problem 1.1 with signal-to-noise ratio τ, with probability 1 − O(n−10 ), there exists a solution to the degree-4 sum-of-squares relaxation for the MLE problem with objective value at least τ that does not depend on the planted vector v. In particular, no algorithm can reliably re ...
Representation of a three dimensional moving scene 0.1
... Any matrix which satisfies the above identity is called an orthogonal matrix. Since r1 , r2 , r3 form a right-handed frame, we further have that the determinant of Rwc must be positive 1. This can be easily seen when looking at the determinant of the rotation matrix: detR = r1T (r2 × r3 ) . which is ...
... Any matrix which satisfies the above identity is called an orthogonal matrix. Since r1 , r2 , r3 form a right-handed frame, we further have that the determinant of Rwc must be positive 1. This can be easily seen when looking at the determinant of the rotation matrix: detR = r1T (r2 × r3 ) . which is ...
