Personalised interactive systems such as recommender require selecting relevant items from massive catalogs dependent on context. Reward-driven offline optimisation of these can be achieved by a relaxation the discrete problem resulting in policy learning or REINFORCE style algorithms. Unfortunately, this step requires computing sum over entire catalogue making complexity evaluation gradient (a...

In this paper, we introduce a model  to optimization of milk run system that is one of VRP problem with time window and uncertainty in inventory. This approach led to the routes with minimum cost of transportation while satisfying all inventory in a given bounded set of uncertainty .The problem is formulated as a robust optimization problem. Since the resulted problem illustrates that grows up ...

This thesis explores novel parameterization concepts for large scale topology optimization that enables the use of evolutionary algorithms in large-scale structural design. Specifically, two novel parameterization concepts based on generative algorithms and Boolean random networks are proposed that facilitate systematic exploration of the design space while limiting the number of design variabl...

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...

1. Abstract Evolutionary topology optimization using design space adjustment and refinement is proposed to deal with large-scale problems. Until now, parallel processing methods have been mainly employed, but they need special treatment in the analyses and algorithms. Design space optimization proposed in this paper is a new efficient method for large-scale topology optimization by virtue of tw...

This article presents the novel breakthrough general purpose algorithm for very large-scale optimization problems. The novel algorithm is capable of achieving breakthrough speeds for very large-scale optimization on general purpose laptops and embedded systems. Application of the algorithm to the Griewank function was possible in up to 1 billion decision variables in double precision took only ...

We consider a new class of huge-scale problems, the problems with sparse subgradients. The most important functions of this type are piece-wise linear. For optimization problems with uniform sparsity of corresponding linear operators, we suggest a very efficient implementation of subgradient iterations, which total cost depends logarithmically in the dimension. This technique is based on a recu...

The sequential quadratic programming (SQP) algorithm has been one of the most successful general methods for solving nonlinear constrained optimization problems. We provide an introduction to the general method and show its relationship to recent developments in interior-point approaches. We emphasize large-scale aspects.

Optimal transport (OT) defines a powerful framework to compare probability distributions in a geometrically faithful way. However, the practical impact of OT is still limited because of its computational burden. We propose a new class of stochastic optimization algorithms to cope with large-scale OT problems. These methods can handle arbitrary distributions (either discrete or continuous) as lo...