Sentence examples for minimized by solving from inspiring English sources

Also, performance of the estimator and the state feedback controller are minimized by solving convex optimization problems.

A feature of the proposed approach is that an upper bound on the guaranteed cost is minimized by solving an optimization problem with linear matrix inequalities.

A feature of the proposed method is that an upper bound on the guaranteed cost is minimized by solving a convex optimization problem with linear matrix inequalities.

In particular, robust stability is guaranteed and a nominal quadratic cost index is minimized by solving an equivalent nonconvex semi-infinite optimization problem.

In the literatures, it is shown that the solution to a constant weight problem can be efficiently obtained in the frequency domain without iterations, whereas the function with the varying weights can be minimized by solving a large sparse linear equation or by iterative methods such as conjugate gradient or preconditioned conjugate gradient (PCG) methods.

The reachable set of system (1) can be minimized by solving the following optimization problem for a scalar (delta>0): begin{aligned} &min bar{delta}quad biggl(bar{delta}= frac{1}{delta}biggr) &mbox{s.t.} begin{cases} (mathrm{a}) &Pgeqdelta I, (mathrm{b}) &(6 mbox 7)mbox{ or }(22 mbox(23) mbox{ or }(26 mbox(26 mbox

Given the known quantized channel H ̂, we attempt to minimize the WMMSE by solving the following optimization minimize W, γ E Σ 1 2 G u - γ G - 1 Σ - 1 2 y 2 H ̂ subject to Tr W W H ≤ γP.

Secondly, the optimal formation-flying trajectory is obtained to minimize the mission time by solving the established optimization model for maximizing the minimum monitoring profit of all GEO satellites.

First a scaled state vector is defined such that the objective function contours in the defined optimization problem become vertical or horizontal ellipses or circles, and then the control input is determined at each sampling time as a state feedback that minimizes the infinite horizon objective function by solving some linear matrix inequalities.

The weights can be found by solving a minimization problem i.e., minimize the error variance subject to (lambda_{i}).

The approximation coefficients are then chosen to minimize an objective function at each point by solving an equality constrained least squares.