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Optimization is important in science and engineering as a way of finding ”optimal” situations, designs or operating conditions. Optimization is typically performed on the basis of a mathematical model of the process under investigation. In practice, optimi ...
Dynamic programming is an algorithmic technique to solve problems that follow the Bellman’s principle: optimal solutions depends on optimal sub-problem solutions. The core idea behind dynamic programming is to memoize intermediate results into matrices to ...
In many real-life optimization problems involving multiple agents, the rewards are not necessarily known exactly in advance, but rather depend on sources of exogenous uncertainty. For instance, delivery companies might have to coordinate to choose who shou ...
Substitutability and interchangeability in constraint satisfaction problems (CSPs) have been used as a basis for search heuristics, solution adaptation and abstraction techniques. In this paper, we consider how the same concepts can be extended to soft con ...
This paper focuses on the two dimensional rectangular non-oriented guillotine cutting stock problem (TDRCSP) in which many pieces with different dimensions need to be cut with different quantities in order to satisfy customers' orders. In order to maximise ...
We describe an approximate dynamic programming method for stochastic control problems on infinite state and input spaces. The optimal value function is approximated by a linear combination of basis functions with coefficients as decision variables. By rela ...
The Distributed Constraint Optimization (DCOP) framework can be used to model a wide range of optimization problems that are inherently distributed. A distributed optimization problem can be viewed as a problem distributed over a set of agents, where agent ...
We consider a source that would like to communicate with a destination over a layered Gaussian relay network. We present a computationally efficient method that enables to select a near-optimal (in terms of throughput) subnetwork of a given size connecting ...
Real world planning applications typically involve making decisions that consumes limited resources, which requires both planning and scheduling. In this paper we propose a new approach that bridges the gap between planning and scheduling by explicitly mod ...
The Upper Confidence Bounds (UCB) algorithm is a well-known near-optimal strategy for the stochastic multi-armed bandit problem. Its extensions to trees, such as the Upper Confidence Tree (UCT) algorithm, have resulted in good solutions to the problem of G ...
