Divide an Array Into Subarrays With Minimum Cost I - Solution & Explanation

The Dynamic Programming approach helps efficiently explore all possible ways to partition the array into subarrays and track the minimal cost achievable. The cost of any subarray is the value of its first element, and in this problem, you can consider breaking down the array such that each partition minimizes the starting costs. You essentially create a function that recursively finds the best division points that lead to the minimal overall start cost of subarrays.

The solution iterates through possible divisions of the array into three subarrays. We continuously check the cost of potential first elements of the subarrays and maintain the minimal cost found. This nested loop approach explores all configurations efficiently given constraints.

The Greedy approach involves picking partitions of the array based on specific criteria, optimizing each choice to attempt to achieve a globally minimal result. Here, we can focus on selecting ends that guarantee maximizing the cost reduction across splits. Breaking the array in greedy increments may be explored due to small size constraints.

This solution uses a simplified greedy technique, returning the sum of first possible fixed elements of subarrays, assuming that such setup tends to produce generally minimal starting sum. Note: This basic attempt showcases logic's potential, ensuring complete solution iteration might still hold most benefits.