Abstract: Finding critical links and weak points is an important task in almost all types of networks. Minimum cuts provide useful information about the critical links. However, finding a minimum cut ...

Combinatorial optimization underpins applications in artificial intelligence, logistics, and network design, yet classical techniques such as greedy search and dynamic programming struggle to balance ...

In today’s competitive business environment, precise scheduling is the best tool for survival, success, and growth. Scheduling is the optimal allocation of resources. In a manufacturing environment, ...

A Greedy algorithm is a strategy used in problem-solving that makes the best optimization choice at each step. This means that at each step or stage of a problem, the algorithm selects the most ...

Abstract: Compressed sensing (CS) algorithms are used for linear array synthetic aperture radar (LASAR) three-dimensional (3D) imaging. However, it is difficult to obtain imaging results with both ...

An algorithm that constructs the final answer by choosing most optimal choice at the moment, not taking any choices back. Given coins = {1,2,5,10,20,50,100,200} and integer n, determine the minimum ...

An algorithm is a step-by-step procedure or formula for solving a problem. In the context of computer science, it is a series of instructions that are executed to accomplish a specific task. These ...

Machine learning holds the potential to solve many real-world problems, but interpretability is a necessary prerequisite for practitioners in high-stakes domains such as medicine and law. Decision ...

In the bin packing problem, objects with different volumes are packed into a finite number of bins in an order that minimizes the number of bins used. This Python program uses three greedy ...

Worse-case analysis takes a “Murphy’s Law” approach to algorithm analysis, which is too crude to give meaningful algorithmic guidance for many important problems, including linear programming, ...