algo-knapsack

name	algo-knapsack
description	Solve 0/1, unbounded, subset-sum, and bitmask set-cover knapsack DP in Python with traceback and O(capacity)-space optimization. Use when picking an optimal subset under a budget/capacity constraint — gene panel or assay selection under a sequencing budget, primer/reagent allocation, experiment portfolio selection, or any "maximize value subject to a cost limit" or "does a subset sum to X" problem.
tool_type	python
primary_tool	python

Knapsack Problems

When to Use

• Choosing which items to include under a hard budget/capacity while maximizing total value (e.g. selecting genes for a targeted panel within a sequencing-cost budget).
• Deciding if reusable resources (primers, reagents) can be repeatedly drawn to maximize yield within a budget — unbounded knapsack.
• Checking whether a subset of costs/weights can hit an exact target (subset sum), e.g. "can these samples' costs sum to exactly the remaining budget?".
• Picking the minimum-cost combination of assays/library preps that jointly covers a required set of genomic regions/features — bitmask set cover.
• You need not just the optimal value but which items were chosen (traceback) or need to fit large capacities in limited memory (space optimization).

Version Compatibility

Pure Python ≥3.8, no dependencies (stdlib only). Logic is unchanged across Python versions.

Prerequisites

• Comfortable with basic dynamic programming / tabulation (see algo-tabulation, algo-intro-memoization).
• No external packages required — pip install nothing.

Goal: maximize total value of selected items without exceeding a weight/cost capacity, each item usable at most once (0/1 knapsack).

Approach: build dp[i][w] = best value using the first i items with capacity w; each cell either skips item i-1 or takes it if it fits.

def knapsack_01(weights, values, capacity):
    """0/1 knapsack: each item used at most once. O(n*capacity) time/space."""
    n = len(weights)
    dp = [[0] * (capacity + 1) for _ in range(n + 1)]
    for i in range(1, n + 1):
        for w in range(capacity + 1):
            dp[i][w] = dp[i - 1][w]  # don't take item i-1
            if weights[i - 1] <= w:
                dp[i][w] = max(dp[i][w], dp[i - 1][w - weights[i - 1]] + values[i - 1])
    return dp[n][capacity]


# Example: pick genes for a targeted panel within a sequencing-cost budget
genes = ['BRCA1', 'TP53', 'EGFR', 'KRAS', 'MYC', 'PTEN']
costs = [50, 30, 40, 35, 45, 25]        # sequencing cost units
relevance = [90, 85, 70, 75, 60, 65]    # clinical relevance score
budget = 100
print(knapsack_01(costs, relevance, budget))  # -> 175 (TP53 + PTEN + ... )

Traceback: which items were chosen

def knapsack_01_with_items(weights, values, names, capacity):
    """0/1 knapsack that also returns the selected item names."""
    n = len(weights)
    dp = [[0] * (capacity + 1) for _ in range(n + 1)]
    for i in range(1, n + 1):
        for w in range(capacity + 1):
            dp[i][w] = dp[i - 1][w]
            if weights[i - 1] <= w:
                dp[i][w] = max(dp[i][w], dp[i - 1][w - weights[i - 1]] + values[i - 1])

    selected = []
    w = capacity
    for i in range(n, 0, -1):
        if dp[i][w] != dp[i - 1][w]:   # value changed => item i-1 was taken
            selected.append(names[i - 1])
            w -= weights[i - 1]
    return dp[n][capacity], selected[::-1]


value, chosen = knapsack_01_with_items(costs, relevance, genes, budget)
print(value, chosen)

Space-optimized 0/1 — O(capacity) space

Traverse capacity backwards so each item is only ever applied once per row (prevents reusing the same item twice, which would make it unbounded). Loses traceback — use the 2D table above if you need the selected items.

def knapsack_01_space_optimized(weights, values, capacity):
    """O(capacity) space 0/1 knapsack. Inner loop MUST go backwards."""
    dp = [0] * (capacity + 1)
    for i in range(len(weights)):
        for w in range(capacity, weights[i] - 1, -1):
            dp[w] = max(dp[w], dp[w - weights[i]] + values[i])
    return dp[capacity]

Unbounded knapsack — items can be reused

Goal: maximize value when each item can be picked any number of times (e.g. a reagent/primer mix drawn repeatedly against a budget).

Approach: iterate capacity forward so an item's own update earlier in the same pass is visible to later capacities — that's what allows reuse.

def knapsack_unbounded(weights, values, capacity):
    """Unbounded knapsack: each item reusable any number of times."""
    dp = [0] * (capacity + 1)
    for w in range(1, capacity + 1):
        for i in range(len(weights)):
            if weights[i] <= w:
                dp[w] = max(dp[w], dp[w - weights[i]] + values[i])
    return dp[capacity]


# Example: PCR primer pairs, synthesis cost vs. amplification efficiency
primer_costs = [2, 3, 4]
primer_efficiency = [3, 4, 5]
reagent_budget = 10
print(knapsack_unbounded(primer_costs, primer_efficiency, reagent_budget))

Subset sum — decision problem, not optimization

Goal: determine if any subset of costs sums to an exact target (boolean DP, not a max-value DP).

def subset_sum(nums, target):
    """True if some subset of nums sums exactly to target."""
    dp = [False] * (target + 1)
    dp[0] = True
    for num in nums:
        for t in range(target, num - 1, -1):
            dp[t] = dp[t] or dp[t - num]
    return dp[target]


print(subset_sum(costs, 80))  # can genes be selected costing exactly 80 units?

Bitmask DP — minimum-cost coverage of required regions/features

Goal: pick the cheapest combination of assays/library-prep methods whose combined coverage (a bitmask of regions/features each one covers) includes every required region.

Approach: dp[mask] = min cost to reach coverage exactly mask; since mask | coverage[i] >= mask always, scanning masks in increasing numeric order and relaxing forward correctly propagates costs (no need to revisit).

def min_cost_coverage(costs, coverage, required):
    """
    Bitmask set-cover DP. `coverage[i]` is a bitmask of regions method i covers;
    `required` is the bitmask of all regions that must be covered.
    Returns minimum total cost, or -1 if `required` is unreachable.
    """
    INF = float('inf')
    dp = [INF] * (required + 1)
    dp[0] = 0
    for mask in range(required + 1):
        if dp[mask] == INF:
            continue
        for i, cov in enumerate(coverage):
            new_mask = mask | cov
            if new_mask <= required:
                dp[new_mask] = min(dp[new_mask], dp[mask] + costs[i])
    return dp[required] if dp[required] != INF else -1


# Regions: 0=Exome, 1=Promoters, 2=Enhancers, 3=Introns
method_costs = [300, 90, 80, 120, 150]
method_coverage = [0b1111, 0b0110, 0b0100, 0b1001, 0b1011]
print(min_cost_coverage(method_costs, method_coverage, required=0b1111))

Pitfalls

• 0/1 vs. unbounded: the direction of the inner capacity loop (backward vs. forward) is the entire difference — get it backward in unbounded or forward in 0/1 and results silently become wrong (not a crash).
• Space-optimized 0/1 loses traceback; keep the full 2D dp table if you need to report which items were selected.
• Subset sum is a boolean/decision DP — don't conflate it with the value-maximizing knapsack DPs.
• Bitmask DP array size is required + 1 (mask values are integers up to the target bitmask, not "number of items") — sizing it by item count is a common off-by-magnitude bug.
• All variants assume non-negative integer weights/costs; capacity must fit in memory as O(capacity) or O(n * capacity) array — reject/rescale fractional or huge real-valued costs first.

See Also

• algo-tabulation — general bottom-up DP tabulation patterns these knapsack variants build on.
• algo-intro-memoization — top-down/recursive DP alternative before converting to tabulation.
• algo-sequence-alignment — another 2D DP (edit distance) using the same table-fill technique.
• bio-experimental-design-sample-size — real budget-constrained experiment selection this DP can drive.