Combinatorial optimization is a field of mathematical optimization that focuses on finding the best solution from a finite set of possible solutions. It often involves problems where the goal is to maximize or minimize a particular objective, such as cost, distance, or time, while satisfying certain constraints. Common examples include the traveling salesman problem and knapsack problem. This area of study is widely applicable in various fields, including computer science, operations research, and logistics. Techniques used in combinatorial optimization include dynamic programming, greedy algorithms, and branch and bound methods, which help efficiently explore the solution space to find optimal or near-optimal solutions.