pack-skill

name

pack-skill

description

Solve any problem in the philosophical family of the knapsack problem (背包问题) using the unified state-design framework from 崔添翼《背包问题九讲》. Use whenever a problem reduces to "choose a subset/multiset of items under a capacity-like cost to optimize a value-like objective" — including 0/1 pack, unbounded/complete pack, bounded/multiple pack, mixed pack, 2-D cost pack, grouped pack, dependency pack (主件/附件, tree-shaped), generalized items (泛化物品), subset-sum / can-it-fill-exactly, counting feasible or optimal schemes, recovering the chosen subset, lexicographically smallest plan, and Kth-best value. Trigger words — 背包/knapsack, subset sum, "choose items with weight/cost ≤ W to maximize value", "ways to make change", "partition into K groups", "limited copies", "items depend on each other", "fill exactly", coin change counting, NOIP-style 金明的预算方案 / 采药 / 装箱问题.

pack-skill — 背包问题九讲 in skill form

What "philosophically a knapsack problem" means

A problem belongs to this family iff it can be cast as:

Given a finite collection of items, each consuming some cost(s) when taken and yielding some value, choose a feasible selection under a capacity to optimize an aggregate of values.

Under that frame, every variant reduces to one master DP: F[i, v] = combine over item-i policies of F[i−1, v − cost(policy)] ⊕ value(policy). The job of this skill is to (a) recognize the variant, (b) write the right state + transition + iteration order, (c) handle the question-form (max value / count / output plan / Kth-best).

Workflow — apply in order

1. Recognize the variant. Read references/01-recognize.md and run the decision tree there. Identify: how many copies per item, single/multi cost, item interactions (exclusive groups, dependencies), and what the question asks.
2. Design the state. Read references/02-state-design.md. Decide: dimensions of F, init values (max-value vs exact-fill), and 1-D space compression direction.
3. Pick the canonical procedure for each item type and call it in the right loop order. The procedures are reusable Python functions in scripts/pack_primitives.py:
   • zero_one_pack(F, C, W) — item taken 0 or 1 time
   • complete_pack(F, C, W) — item taken unlimited times
   • multiple_pack(F, C, W, M) — item taken 0..M times (binary-splitting)
4. Handle the question form. Output plan, count schemes, count optimal schemes, Kth-best — see references/11-variations.md and the corresponding scripts.
5. Verify correctness on the simple cases first — for any non-trivial setup, brute-force enumerate up to N≤20 and diff against the DP. scripts/pack_primitives.py includes a brute_max helper for this.

Reference map

Read the file matching the variant you identified in step 1. Each is self-contained.

Variant	Reference file	Script
Recognize / decision tree	references/01-recognize.md	—
State design, init, space opt	references/02-state-design.md	—
01 pack (0/1)	references/03-zero-one.md	scripts/solve_01.py
Complete pack (unbounded)	references/04-complete.md	scripts/solve_complete.py
Multiple pack (bounded M_i)	references/05-multiple.md	scripts/solve_multiple.py
Mixed (01 + complete + bounded)	references/06-mixed.md	scripts/solve_mixed.py
2-D cost (two capacities)	references/07-2d-cost.md	scripts/solve_2d.py
Grouped (mutually exclusive groups)	references/08-grouped.md	scripts/solve_grouped.py
Dependency (main / accessory, tree)	references/09-dependency.md	scripts/solve_dependency.py
Generalized items (泛化物品 abstraction)	references/10-generalized.md	—
Question variations	references/11-variations.md	scripts/count_schemes.py, scripts/count_optimal.py, scripts/output_solution.py, scripts/kth_optimal.py

The five invariants (read once, never forget)

These come straight from chapters 1–9. They are the rules every variant obeys.

1. Master transition. F[i,v] = max{ F[i-1, v - k·Cᵢ] + k·Wᵢ } over the legal copy-counts k for item i. Every variant differs only by which k are legal.
2. 1-D direction encodes copies.
   • 0/1 item → iterate v descending from V to Cᵢ (so each Wᵢ is added at most once).
   • Unbounded item → iterate v ascending from Cᵢ to V (so Wᵢ may cascade).
   • Bounded → binary-split into O(log Mᵢ) 0/1 items.
3. Init encodes "exact fill" vs "≤ capacity".
   • Must fill exactly: F[0]=0, F[1..V] = -∞.
   • Just ≤ V: F[0..V] = 0.
4. Grouped/dependency = mutually exclusive policies per unit. Loop order is for group → for v descending → for item-in-group, never permute these three.
5. 泛化物品 (generalized item) is the universal abstraction. Any item / group / subtree corresponds to a function h: [0..V] → value. "Sum" of two generalized items is f(v) = max_{0≤k≤v} h(k)+l(v-k), O(V²). Most "hard-looking" knapsacks are just a sum of generalized items.

Common pitfalls

• Wrong iteration direction → silently overcounts (treats 0/1 as unbounded) or undercounts.
• Forgetting the exact-fill init → answers max-value when the problem says "fill exactly".
• Grouped pack with v-loop inside item-loop → lets multiple items of the same group be taken.
• Multiple pack with M·C ≥ V → just call complete_pack; don't binary-split.
• Dependency pack as plain group → wrong; you must first 0/1-DP the accessory set into a generalized item of length V − C_main + 1 (see chapter 7).
• Counting schemes → replace max with sum, and init F[0,0]=1 (or F[0]=1 in 1-D).
• Lex-smallest plan → renumber items as x ← N+1−x first, then run the standard recovery (chapter 9.2).

Quick start — solving a fresh problem

Q: N items, each with weight wᵢ and value vᵢ, knapsack capacity W, each item used at most once.
   Maximize total value.

This is plain 0/1 pack. Run:

python3 scripts/solve_01.py --capacity W --items "w1,v1; w2,v2; ..."

Or import the primitive:

from pack_primitives import zero_one_pack
F = [0] * (W + 1)
for c, w in items:
    zero_one_pack(F, c, w)
print(F[W])             # max value with capacity ≤ W

For "fill exactly W": initialize F = [0] + [float('-inf')] * W instead.

When the problem doesn't look like a knapsack but is one

Recognize these disguises (each is treated in references/01-recognize.md):

• Coin change — number of ways: complete pack + sum instead of max, F[0]=1.
• Subset sum / partition equal subset: 0/1 pack with W=value, ask "is F[target] reachable".
• Cutting rod: complete pack with C = length, W = price.
• Target sum / +/- signs: shift to subset-sum form target = (S + target_diff)/2.
• NOIP 金明的预算方案: dependency pack (main + ≤2 accessories) — see chapter 7.
• NOIP 采药 / 装箱问题: textbook 0/1 pack.
• Multi-dimensional resource scheduling (cost in cash AND time, etc.): 2-D cost pack.

Verification

Whenever you write a new knapsack DP, brute-force check it on small inputs:

from pack_primitives import brute_max_01
assert dp_answer == brute_max_01(items, W)

scripts/pack_primitives.py ships brute_max_01, brute_max_complete, brute_max_multiple for this.