This study has been inspired by the paper "An efficient 3D topology optimization code written in MATLAB” written by Liu and Tovar (2014) demonstrating that SIMP-based three-dimensional (3D) topology optimization of continuum structures can be implemented in 169 lines of MATLAB code. Based on the above paper, we show here that, by simple and easy-to-understand modificati...

Bayesian Networks (BN) provides a robust probabilistic method of reasoning under uncertainty. They have been successfully applied in a variety of real-world tasks but they have received little attention in the area of load-frequency control (LFC). In practice, LFC systems use proportional-integral controllers. However since these controllers are designed using a linear model, the nonlinearities...

This paper discusses an implicit reformulation of the MPEC (mathematical program with complementarity constraints) problem in order to solve a robust structural optimization with a non-probabilistic uncertainty model of the static load. We first show the relation among the robust constraint satisfaction, worst scenario detection, and robust structural optimization, and derive the MPEC formulati...

This paper considers safe control synthesis for dynamical systems with either probabilistic or worst-case uncertainty in both the dynamics model and safety constraints. We formulate novel robust (worst-case) Lyapunov function (CLF) barrier (CBF) constraints that take into account effect of case. show formulation leads to a second-order cone program (SOCP), which enables efficient stable synthes...

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of and reasoning with complex, structured probability distributions. Examples include functional languages (Church [Goodman et al., 2008], IBAL [Pfeffer, 2001]), object-oriented languages (Figaro [Pfeffer, 2009]), and logic languages (Pr...

We treat uncertain linear programming problems by utilizing the notion of weighted an-alytic centers and notions from the area of multi-criteria decision making. After introducing ourapproach, we develop interactive cutting-plane algorithms for robust optimization, based on concaveand quasi-concave utility functions. In addition to practical advantages, due to the flexibility of our...

Abstract. We propose an approach to address data uncertainty for discrete optimization and network flow problems that allows controlling the degree of conservatism of the solution, and is computationally tractable both practically and theoretically. In particular, when both the cost coefficients and the data in the constraints of an integer programming problem are subject to uncertainty, we pro...