The fixed-rate entropy-constrained vector quantizer draws its motivation from the large gap in the performance of the optimal entropy-constrained scalar quantizer (ESCQ) and the fixed-rate LMQ for most non-uniform sources and tries to bridge this gap while maintaining a fixed-rate output. Having a fixed-rate output avoids all problems associated with a variable rate output such as error propaga...

We present an algorithm for solving a general min-cut, twoway partitioning problem subject to timing constraints. The problem is formulated as a constrained programming problem and solved in two phases: cut-set minimization and timing satisfaction. A mathematical programming technique based on iterative quadratic programming (TPIQ) is used to find an approximate solution to the constrained prob...

The Chance-Constrained Stochastic Programming (CCSP) is one of the models for decision making under uncertainty. In this paper, we consider the special case of the CCSP in which only the righthand side vector is random with a discrete distribution having a finite support. The unit commitment problem is one of the applications of the special case of the CCSP. Existing methods for exactly solving...

We propose a safe approximation to joint chance-constrained programming, where the constraint functions are additively dependent on normally-distributed random vector. The is analytical, meaning that it requires neither numerical integrations nor sampling-based probability approximations. Under mild assumptions, standard nonlinear program. compare this new another analytical for programming bas...

water is an essential yet constrained commodity for human communities and ecological systems depend on it. with population growth and economic development, water resource is increasingly declining. over the past two decades, because of changes in population and climate, and relative welfare increase, the per capita consumption of water has increased. thus, optimal use of available water resourc...

In practical applications of mathematical programming it is frequently observed that the decision maker prefers apparently suboptimal solutions. A natural explanation for this phenomenon is that the applied mathematical model was not sufficiently realistic and did not fully represent all the decision makers criteria and constraints. Since multicriteria optimization approaches are specifically d...

This work addresses the topic of constrained dynamic programming for problems involving multi-stage mixed-integer linear formulations with a linear objective function. It is shown that such problems may be decomposed into a series of multi-parametric mixed-integer linear problems, of lower dimensionality, that are sequentially solved to obtain the globally optimal solution of the original probl...

Ten years after the Internet revolution are we standing on the brink of another revolution: networked embedded systems that connect the physical world with the computers, enabling new applications ranging from environmental monitoring and wildlife tracking to improvements in health care and medicine. Only 2% of all microprocessors that are sold today are used in PCs; the remaining 98% of all mi...

In this paper, we deal with a hydraulic reservoir optimization problem with uncertainty on inflows in a joint chance constrained programming setting. In particular, we will consider inflows with a persistency effect, following a causal time series model, and examine the impact of the ”Gaussian” assumption for such inflows. We present an iterative algorithm for solving similarly structured joint...

We follow the popular approach for unconstrained minimization, i.e. we develop a local quadratic model at a current approximate minimizer in conjunction with a trust region. We then minimize this local model in order to nd the next approximate minimizer. Asymptot-ically, nding the local minimizer of the quadratic model is equivalent to applying Newton's method to the stationarity condition. For...