Sentence examples for use of convex optimization from inspiring English sources
With the use of convex optimization techniques, the deterministic problem is reformulated to a semidefinite programming (SDP) problem which can be efficiently solved.
We approach the ML estimation problem by solving a simplified and more tractable problem which allows the use of convex optimization tools for its distributed solution.
In addition, the use of convex optimization puts existing concepts for surrogate representation on a more rigorous basis and several conclusions are drawn, particularly on the importance of specific CPTs and weighting factors of regression-based approaches.
A novel approach to address the latter is performed that entails a suitable transformation, which allows the use of convex optimization and also forms the basis for the design of a distributed PCC algorithm via performing two-level primal-dual decomposition.
To that end, a novel approach to address that latter is performed that entails a suitable transformation, which allows the use of convex optimization and also forms the basis for the design of a distributed PCC algorithm via performing two-level primal-dual decomposition.
For example, Candès & Recht [2], and Koltchinskii et al. [5] reconstruct the matrix by assuming that the data structure is low-rank, and this is useful since it enables us to use a popular method of convex optimization.
This relation triggers the exploration of several interpolation possibilities within the same context, which uses the theory of convex optimization to minimize quadratic functions with linear constraints.
Subsequently, the explicit characterization of the resilient control gain is obtained by means of linear matrix inequalities that can be effectively solved by using the ideas of convex optimization.
Since this optimization problem is convex with respect to the variables to be optimized, we can use a popular method of convex optimization (e.g., Boyd & Vandenberghe [1]).
This paper presents how commonly used machine learning classifiers can be analyzed using a common framework of convex optimization.
Then, design problem is formulated by a set of linear/bilinear matrix inequalities and then solved using a new iterative algorithm in the form of convex optimization problem.
