The phrase "a vector that solves" is correct and usable in written English.
It can be used in mathematical or scientific contexts when discussing vectors that provide solutions to equations or problems.
Example: "In this study, we will identify a vector that solves the linear equation presented in the previous section."
Alternatives: "a vector that provides a solution" or "a vector that addresses".
Using Lagrange multiplier technique, the weight vector that solves this equation is given by: (10).
However, we are interested on the point in Q TC that maximizes the sum rate, which implies that the target vector that solves problem (7) must lie in the boundary of Q TC.
Add the new link rate vector, that solves the scheduling subproblem, to J. Repeat the process until u u - u l < ε, or when the generated column is already present in J.
2) Solve the restricted master problem to optimality with a primal-dual interior-point method [19] to obtain the optimal Lagrangian multipliers or capacity demands λ*. 3) Add the new link rate vector, that solves the scheduling subproblem, to J. 4) Repeat the process until u u - u l < ε, or when the generated column is already present in J. .
A P-invariant is a non-zero vector y that solves Ay = 0, and a T-invariant is a non-zero, non-negative vector, x, that solves A T x = 0. P-invariants correspond to conservation laws of the network, while T-invariants represent the sequence of transitions that lead back to the initial marking [ 21].
This paper deals with the problem of reconstruction of unknown encoded vector that is solved by means of procedure of adaptive observer design for chaotic Duffing system.
"Music was a vector that we wanted to build a universe around," says Bangalter.
Here, we describe a method that solves this problem.
We also show an algorithm that solves the restricted problem.
A vector ( {overline{mathbf{t}}}_I ) that solves the FPP satisfies (6.2) on each of its components: so its ( {overline{t}}_i ) is a correct saturation time for lot i.
This paper contains the explanation on how the Householder transformation, which is extensively used for matrix orthogonalization, provides an elegant analytical expression that solves the vector orthogonalization problem.
