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Workflows
Coding workflows · Matrix-chain cost planning
03
A concrete starting point
Find the minimum scalar-multiplication cost for a chain of up to 12 matrices. Test one matrix and different parenthesizations, rejecting dimensions outside 1 through 1000 without multiplying any matrix entries.
Define the change
Describe the input, expected output and boundaries that must stay intact.
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Review the proposed edits and any commands before relying on their result.
Check the outcome
Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
Matrix-chain cost planning
A concrete starting point
def chain_cost(dimensions):
if type(dimensions) not in (list, tuple) or not 2 <= len(dimensions) <= 13:
raise ValueError("one through twelve matrices")
if any(type(d) is not int or not 1 <= d <= 1000 for d in dimensions):
raise ValueError("dimensions 1 through 1000")
n = len(dimensions)-1
cost = [[0]*n for _ in range(n)]
for width in range(2, n+1):
for left in range(n-width+1):
right = left+width-1
cost[left][right] = min(
cost[left][k]+cost[k+1][right]+
dimensions[left]*dimensions[k+1]*dimensions[right+1]
for k in range(left,right))
return cost[0][n-1]The example is a starting point for a real project, not a claim that generated code is already tested. Adjust paths, dependencies and expected behavior to match your application.