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The stabilization conditions are formulated as linear matrix inequalities (LMIs) which can be solved by convex optimization algorithm.
For the stochastic correlated model, we have shown that these two problems can be solved by convex optimization.
Applying an approximate form of Shannon capacity, the power allocation problem can be solved by convex optimization and geometric programming tools efficiently.
In this paper, we show that a problem of finding a permuted version of k vectors from RN such that they belong to a prescribed rank r subset, can be solved by convex optimization.
A nonsingular state transformation together with using the S-procedure and also slack variables lead to derive the stabilization conditions in the formulation of linear matrix inequalities which can be solved by convex optimization techniques.
This makes the solution of sum capacity of MIMO BC possible, since the solution of sum capacity of MIMO BC is in general a non-convex optimization, while MIMO multiple access channel can be solved by convex optimization.
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In order to achieve this, the problem of estimating 4-D light field is formed as energy minimization problem with several constraints and the problem is solved by convex optimization.
In [8], the joint linear precoding and power allocation for multiuser MIMO systems with CoMP are solved by convex optimization techniques under per-BS power constraints to improve the system performance.
Return from DC programming to convex programming DCA consists in an iterative approximation of a DC program by a sequence of convex programs that will be solved by appropriate convex optimization algorithms.
Equation (8) doesn't have an analytical solution but can be transformed into a convex quadratic form, which can be solved by standard convex optimization methods, such as interior point methods [ 22], as given below: where the new variable u ∈ R n provides constraints on x.
Problem (1) can be solved by using convex optimization techniques.
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