This article designs a Rayleigh fading channel simulator using IIR filter (called Doppler filter), which is used to approximate the Jakes Doppler spectrum. We mainly focus on the design of Doppler filter and model it as an optimization problem. Non-convexity of the problem is proved in this paper and genetic algorithm (GA) is used to optimize it, which has never been used before in this area. S...

‎In this paper‎, ‎some properties of pre-monotone operators are proved‎. ‎It is shown that in a reflexive Banach space‎, ‎a full domain multivalued $sigma$-monotone operator with sequentially norm$times$weak$^*$ closed graph is norm$times$weak$^*$ upper semicontinuous‎. ‎The notion of $sigma$-convexity is introduced and the‎ ‎relations between the $sigma$-monotonicity and $sigma$-convexity is i...

In this paper, we study a nonsmooth/nonconvex multiobjective optimization problem with uncertain constraints in arbitrary Asplund spaces. We first provide necessary optimality condition fuzzy form for approximate weakly robust efficient solutions and then establish theorem quasi-efficient of the sense limiting subdifferential by exploiting terms Fréchet subdifferential. Sufficient conditions (w...

A dynamical annealing algorithm for global optimization based on approximate solution of the Smoluchowski equation is presented. The equations of motion in the Gaussian density approximation are interpreted as a steepest descent quench on a time dependent effective potential energy surface. A relation between the convexity condition for the effective potential surface and the size of thermal fl...

We present distribution-free generalization error bounds which apply to a wide class of approximate Bayesian Gaussian process classification (GPC) techniques, powerful nonparametric learning methods similar to Support Vector machines. The bounds use the PACBayesian theorem [8] for which we provide a simplified proof, leading to new insights into its relation to traditional VC type union bound t...

This paper proposes sampling techniques to approximate the configuration space for optimal motion planning. We sample valid configurations in the workspace and construct path subconvex cells in the free configuration space. The radius of each cell is calculated using lower bounds on the robot’s minimum time to collision. Using theorems about path convexity, the shortest paths found between any ...

In this paper, we consider two sequential decision making problems with a convexity structure, namely an energy storage optimization task and a multi-product assembly example. We formulate these problems in the stochastic programming framework and discuss an approximate dynamic programming technique for their solutions. As the cost-to-go functions are convex in these cases, we use max-affine es...

In this research a new algebraic semantics of rough set theory including additional meta aspects is proposed. The semantics is based on enhancing the standard rough set theory with notions of ’relative ability of subsets of approximation spaces to approximate’. The eventual algebraic semantics is developed via many deep results in convexity in ordered structures. A new variation of rough set th...

The task of generating time optimal trajectories for a six degrees of freedom industrial robot is discussed and an existing convex optimization formulation of the problem is extended to include new types of constraints. The new constraints are speed dependent and can be motivated from physical modeling of the motors and the drive system. It is shown how the speed dependent constraints should be...

In this paper, we propose the first exact algorithm for minimizing the difference of two submodular functions (D.S.), i.e., the discrete version of the D.C. programming problem. The developed algorithm is a branch-and-bound-based algorithm which responds to the structure of this problem through the relationship between submodularity and convexity. The D.S. programming problem covers a broad ran...