Abstract Robust optimization is proving to be a fruitful tool study problems with uncertain data. In this paper we deal the minmax aproach robust multiobjective optimization. We survey main features of problem particular reference results concerning linear scalarization and sensitivity optimal values respect changes in uncertainty set. Furthermore prove solutions Finally apply presented mean-va...

open-pit production scheduling (opps) problem focuses on determining a block sequencing and scheduling to maximize net present value (npv) of the venture under constraints. the scheduling model is critically sensitive to the economic value volatility of block, block weight, and operational capacity. in order to deal with the opps uncertainties, various approaches can be recommended. robust opti...

This paper extends the Log-robust portfolio management approach to the case with short sales, i.e., the case where the manager can sell shares he does not yet own. We model the continuously compounded rates of return, which have been established in the literature as the true drivers of uncertainty, as uncertain parameters belonging to polyhedral uncertainty sets, and maximize the worst-case por...

We consider optimal decision-making problems in an uncertain environment. In particular, we consider the case in which the distribution of the input is unknown, yet there is some historical data drawn from the distribution. In this paper, we propose a new type of distributionally robust optimization model called the likelihood robust optimization (LRO) model for this class of problems. In contr...

Seemingly unrelated regressions are statistical regression models based on the Gaussian distribution. They are popular in econometrics but also arise in graphical modeling of multivariate dependencies. In maximum likelihood estimation, the parameters of the model are estimated by maximizing the likelihood function, which maps the parameters to the likelihood of observing the given data. By tran...

Recent developments in the theory of choice under uncertainty and risk yield a pessimistic decision theory that replaces the classical expected utility criterion with a Choquet expectation that accentuates the likelihood of the least favorable outcomes. A parallel theory has recently emerged in the literature on risk assessment. It is shown that a general form of pessimistic portfolio optimizat...

Portfolio Optimization is based on the efficient allocation of several assets, which can get heavily affected by uncertainty in input parameters. So we must look for such solutions give us steady results uncertain conditions too. Recently, optimization problems are being dealt with robust approach. With this development, interest researchers has been shifted toward portfolio optimization. In pa...

Calculating or approximating the derivatives for large-scale multi-dimensional functions is an active research area in modern mathematics. The rationale behind this popularity is its wide applications in statistics, financial mathematics and portfolio optimization. For example, in statistics, maximum likelihood estimation seeks the value of parameter vector that maximize the likelihood function...

ÐImage matching applications such as tracking and stereo commonly use the sum-of-squared-difference (SSD) measure to determine the best match. However, this measure is sensitive to outliers and is not robust to template variations. Alternative measures have also been proposed that are more robust to these issues. We improve upon these using a probabilistic formulation for image matching in term...