Nonconvex embedded optimization: code generation for fast real-time optimization + ROS support - alphaville/optimization-engine
We present here two examples of a large deviations principle where the rate function is not strictly convex. This is motivated by an example from mathematical finance, and adds a new item to the zoology of non strictly convex large deviations. For one of these examples, we also show that ...
In this paper, considering the three nonconvex penalty function, the L1/2 penalty, the MCP penalty and the SCAD penalty. The L1/2 penalty function is \({P}_{{L}_{1/2}}(x;\lambda )=\lambda \parallel x{\parallel }_{1/2}^{1/2}=\mathop{\sum }\limits_{i=1}^{p}| {x}_{...
•Reformulatingnon-convexmodels •Example •Summary •References 2 FORS-iltapäiväseminaari/Lahdelma15.11.20073 Cogeneration •Cogenerationmeansproductionoftwoormoreenergy productstogetherinanintegratedprocess –CHP=combinedheatandpowergeneration ...
Solving the optimal control of stochastic differential equations (SDEs) using the dynamic programming method requires writing the problem in terms of the so-called value function. This paper presents conditions to assure that the value function is convex away from the origin, a concept that allows...
non-convex MINLPs. 3.1 Under- and over-estimators As mentioned in the introduction, even solving the continuous relaxation of a non-convex MINLP is unlikely to be easy. For this reason, a further relaxation step is usual. One way to do this is to replace each non-convex function f j (...
Note that we overload the notation ‖⋅‖ε to represent the Laplace function based surrogate, although it is not a norm. By incorporating (4) into (1), we propose the following tensor completion model: arg minX‖X‖ε,s.t.XΩ=BΩ. In summary, we propose a novel nonconvex ...
which is a convex functional and makes the problem easier to solve. It has been shown that under some assumptions, the regularization problems with suchl1relaxation leads to a near optimal sparse solution. To further encourage the sparsity of the solutions, some nonconvex regularizers are proposed...
Finally, we demonstrate non-convex regularized Huber regression withτcalibrated via a tuning-free procedure. This function is computationally more efficient, because the cross-validation is only applied to choosing the regularization parameter. More details of the tuning-free procedure can be found in...
The purpose of this paper is to study a non-convex fuzzy multi-objective quadratic programming problem, in which both the technological coefficients and resources are fuzzy with nonlinear membership function. A computational procedure to find a fuzzy efficient solution of this problem is developed. A...