Under the proposed model, we calculate Bayesian optimal decision and its approximation algorithm for the difference with and without the treatment for the entire population. We conduct experiments and ...

Stochastic Approximation Algorithms for System Identification, Estimation, and Decomposition of Mixtures ...

Approximation and Online Algorithms. (2021). An Improved Approximation Bound for Minimum Weight Dominating Set on Graphs of Bounded Arboricity. Retrieved from http ...

When you need to solve a hard optimization problem, you might resort to using an approximation algorithm, which is a fast and efficient way to find a near-optimal solution. But how do you measure ...

Greedy algorithms and approximation are two common techniques for solving optimization problems, such as finding the shortest path, the maximum flow, or the minimum spanning tree. But how do you ...

We show (under standard assumptions) that there are NP optimization problems for which estimation is easier than approximation. Namely, one can estimate the value of the optimal solution within a ...

Next, we will study algorithms for NP-hard problems whose solutions are guaranteed to be within some approximation factor of the best possible solutions. Such algorithms are often quite efficient and ...

The algorithm avoids the optimization and linearization, and can be fulfilled in three steps. First construct two parallel quadrates based on the preset two reference points of the spatial line ...

Approximation algorithms for the TSP endeavour to provide efficient, near‐optimal solutions where exact methods prove computationally prohibitive. Seminal approaches, ...