This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) ...

This course covers reinforcement learning aka dynamic programming, which is a modeling principle capturing dynamic environments and stochastic nature of events. The main goal is to learn dynamic ...

Dynamic programming (DP) algorithms have become indispensable in computational biology, addressing problems that range from sequence alignment and phylogenetic inference to RNA secondary structure ...

This is an advanced undergraduate course on algorithms. This course examines such topics as greedy algorithms, dynamic programming, graph algorithms, string processing, and algorithms for ...

Dynamic Programming and Optimal Control is offered within DMAVT and attracts in excess of 300 students per year from a wide variety of disciplines. It is an integral part of the Robotics, System and ...

This is a preview. Log in through your library . Abstract We addressed the problem of developing a model to simulate at a high level of detail the movements of over 6,000 drivers for Schneider ...

With new DPX instructions, the NVIDIA Hopper GPU architecture introduced today at GTC will speed dynamic programming — a problem-solving approach utilized in algorithms for genomics, quantum computing ...

In 1958, Wagner and Whitin published a seminal paper on the deterministic uncapacitated lot-sizing problem, a fundamental model that is embedded in many practical production planning problems. In this ...

The Optimisation and Algorithms (OA) Group conducts research and teaching in the theory and applications of Operations Research (OR). The group's foremost interest is in fundamental research in OR and ...

Dynamic programming (DP) algorithms have become indispensable in computational biology, addressing problems that range from sequence alignment and phylogenetic inference to RNA secondary structure ...