Non-convex Problems

Non-convex problems are complex optimization challenges where the solution space contains multiple local optima—peaks and valleys—making it difficult to find the best overall solution. Unlike convex problems, which have a single global optimum that can be efficiently identified, non-convex problems may require exploring many different solutions and risks getting stuck in suboptimal results. These issues are common in real-world scenarios like machine learning, engineering, and finance, where the landscape of possible solutions isn’t straightforward, demanding advanced methods and significant computation to find near-optimal solutions.