1. An FPTAS for Counting Integer Knapsack Solutions Made Easy
Nir Halman
Hebrew University of Jerusalem

2. The Integer Knapsack Problem
Input: n items. Item i has: weight wi , value vi and limit ui on #copies (U=max ui). Knapsack capacity C.
Output: Max 𝑖≤𝑛 𝑣 𝑖 𝑥 𝑖
s.t.
𝑖≤𝑛 𝑤 𝑖 𝑥 𝑖 ≤𝐶; 0≤ 𝑥 𝑖 ≤ 𝑢 𝑖
Admits an FPTAS [IK75,HS76]
If all ui=1, called 0/1 knapsack
Counting problem: How many feasible solutions exist? (v is obsolete)
Theorem. An O( 𝑛 3 log 3 𝑈log𝐶 𝜖 log 𝑛log 𝑈 𝜀 ) time FPTAS
Improves over Gopalan et al. [FOCS11] by 𝑂 ( 𝑛 2 𝜀 )
Other well-know counting problems: counting satisfying assignments of distinctive normal form (DNF) formulas (1st FPRAS by [KL85]), counting independent sets in graphs of max. degree ≤5 (1st FPTAS by [We06]).

3. A naïve DP formulation
Let 𝑚 𝑖 (𝑗)=max. #copies of item i (≤ui) using ≤ j space, 𝑠 𝑖 𝑗 =#feasible sols. using ≤ j space & items 1,…,i Recurrence: 𝑠 1 𝑗 = 𝑚 1 𝑗 +1 𝑗=1,…,𝐶 𝑠 𝑖 𝑗 = 𝑘=0 𝑚 𝑖 𝑗 𝑠 𝑖−1 𝑗−𝑘 𝑤 𝑖 2≤𝑖≤𝑛, 𝑗=1,…,𝐶 Solution = 𝑠 𝑛 𝐶 Running time: i ≤ n items, space j ≤ C and summation of size ≤ U  O(nUC) time, i.e., pseudopolynomial in both U and C

4. A second DP formulation
Idea:  item i break integer decision i into ≤ log ui binary sub-decisions.
If all ui were powers of 2 minus one, then easy: i Break item i into m bundles of weights 𝑤 𝑖 ,2 𝑤 𝑖 ,…,((ui +1)/2-1) 𝑤 𝑖 and solve the corresponding 0/1 knapsack counting problem
E.g., if ui=7 (=111 in binary encoding) and 𝑤 𝑖 =1:

5. A second DP formulation
Idea:  item i break integer decision i into ≤ log ui binary sub-decisions.
But when ui is not a power of 2 minus one, then the bundles are not necessarily “independent”: E.g., if ui=5 (=101 in binary encoding) and 𝑤 𝑖 =1: + -
bit # from left
Corresponding bundle 1 2 3
constraint non-binding
constraint binding - + - + - + - + - 5 4 3 2 1

6. A second DP formulation
Idea: break integer decision i into at most ui binary sub-decisions,  item i
Let:
msb(x,i)=place (from the right) of most significant “1” bit of the ith least significant bits of x, if exists or -∞ e.g.,
msb(5,2)=1 (5 in binary encoding is 101);
msb(4,1)=-∞ (4 in binary encoding is 100).

7. A second DP formulation – cont’
𝑧 𝑖,𝑙,0 𝑗 = 𝑧 𝑖,𝑙−1,0 𝑗 + 𝑧 𝑖,𝑙−1,0 𝑗− 2 𝑙−1 𝑤 𝑖 l=2,… (a) 𝑧 𝑖,𝑙,1 𝑗 = 𝑧 𝑖,𝑙−1,0 𝑗 + 𝑧 𝑖,msb( 𝑢 𝑖 ,𝑙−1),1 𝑗− 2 𝑙−1 𝑤 𝑖 l=2,… (b) 𝑧 𝑖,1,𝑟 𝑗 = 𝑧 𝑖−1,└log+ 𝑚 𝑖 𝑗 ┘+1,1 𝑗 + 𝑧 𝑖−1,└log+ 𝑚 𝑖−1 𝑗 −𝑤 𝑖 ┘+1,1 𝑗− 𝑤 𝑖 (c) E.g., ui=5 (101 in binary encoding) and wi=1. 𝑧 𝑖,3,0 𝑗 considers placing a bundle of 4 and then recursively checks a bundle of 2 by using 𝑧 𝑖,2,0 ∙ 𝑧 𝑖,3,1 𝑗 considers placing a bundle of 4 and then recursively checks a bundle of 1 (because msb(5,2)=1) by using 𝑧 𝑖,1,0 ∙ The last index of z is an indicator for the constraint xi ≤ui being potentially binding
𝑧 𝑖,−∞,1 𝑗 = 𝑧 𝑖−1,└log+ 𝑚 𝑖−1 𝑗 ┘+1,1 𝑗 (d)
𝑧 1,𝑙,𝑟 𝑗 = 𝑚 1 𝑗 l=1,… (e)
𝑧 𝑖,𝑙,𝑟 𝑗 = j<0 (f)
where r=0,1, i=2,...,n and j=0,...,C
Solution = 𝑧 𝑛,└log+ 𝑢 𝑛 ┘+1,1 𝐶
Running time: i ≤ n items, l ≤ log U sub-decisions, r=0,1 space j ≤ C, and summation of size ≤ 2 O(nClogU) time, i.e., pseudopolynomial in C only

8. An FPTAS
Note: All functions 𝑧 𝑖,𝑙,𝑟 ∙ are univariate and monotone non-decreasing in their variable
This is called a monotone dynamic program
Thm[HKLOS14]: A monotone DP with depth T, |action space| ≤A, |state space|≤S and bound M on max. value, admits an O( 𝑇 2 𝜀 𝐴 log𝑆 log𝑀 log 𝑇log𝑀 𝜀 ) FPTAS
 Here T=nlogU, A=2, S=C, and M=Un  O( 𝑛 3 𝜀 log3𝑈 log𝐶 log 𝑛log𝑈 𝜀 ) time

9. K-approximation functions
Assume function φ is defined over integer domains and is non-negative, i.e., φ:[0, U]  R+
We say that φ* is a K-approximation of φ if φ(x) ≤ φ*(x) ≤ Kφ(x), x ∈ [0, U]
We denote this by φ* ≅K φ

10. 1. Calculus of K-apx. Functions
Let α,β ≥ 0, φi* ≅Ki φi , K1 ≥ K2, and i =1,2
Operation name
Operation and apx. ratio
summation
α+βφ1*+φ2* ≅K1 α+βφ1+φ2
minimization
min{φ1*, φ2*} ≅K1 min{φ1, φ2}
composition
φ1*(ψ) ≅K1 φ1(ψ)
approximation
φ2* ≅K1 K2 φ1 when φ2= φ1*
𝑧 𝑖,𝑙,0 𝑗 = 𝑧 𝑖,𝑙−1,0 𝑗 + 𝑧 𝑖,𝑙−1,0 𝑗− 2 𝑙−1 𝑤 𝑖 (𝑎)
𝑧′ 𝑖,𝑙,0 𝑗 = 𝑧∗ 𝑖,𝑙−1,0 𝑗 + 𝑧∗ 𝑖,𝑙−1,0 𝑗− 2 𝑙−1 𝑤 𝑖 (𝑎′)
Corollary: (summation of composition) Let z* in (a’) be K1 -apx. functions of the original functions. Then 𝑧′ 𝑖,𝑙,0 𝑗 ≅K1 𝑧 𝑖,𝑙,0 𝑗

11. 2. K-approximation Sets
Definition: Let φ:[0,...,S]→ R+ be monotone. A K-apx. set of φ is W [0,...,S] with 0,SW and ratio  K between each two consecutive points in W
Construction: φ* is the apx. of φ induced by W: φ ≥K
 K W φ*
Question: How small a K-apx. set of φ can be, and how fast can it be constructed?

12. Approximating a Monotone Function: The Compress Operator
Answer: A monotone φ admits a K-apx. function with |breakpoints| poly. of input size (= log S + log M) via binary search over both domain and range.
We explicitly construct it via the compress procedure:
g := Compress(φ , K=1+ ).
g is a piecewise step function
g ≅K φ
#pieces is O(logK M) = O( log M ε ) (using K<2logK≥ε)
Running time is O( log M log S ε ), query time is O(log log M ε ), assuming pieces are stored as a list (x, φ(x)) sorted by x

13. FPTAS formulation begin 1. K:= 1+𝜖 1 2𝑇 .
2. Define 𝑧∗ 1,𝑙,𝑟 ∙ := 𝑧 1,𝑙,𝑟 ∙
3. for t=2 to depth T do
4. implement 𝑧′ 𝑖,𝑙,𝑟 ∙ oracle using DP and 𝑧∗ 𝑖 ′ , 𝑙 ′ ,𝑟 ∙ where either i’<i or l’<l
5. 𝑧∗ 𝑖,𝑙,𝑟 ∙ :=compress( 𝑧′ 𝑖,𝑙,𝑟 ∙ ,K)
6. endfor
end
Corollary: (summation of composition) Let z* in (a’) be K1 -apx. functions of the original functions. Then 𝑧′ 𝑖,𝑙,0 𝑗 ≅K1 𝑧 𝑖,𝑙,0 𝑗

14. Running Time and Error Analysis
begin
1. K:= 1+𝜖 1 2𝑇 .
2. Define 𝑧∗ 1,𝑙,𝑟 ∙ := 𝑧 1,𝑙,𝑟 ∙
3. for t=2 to depth T do
4. implement 𝑧′ 𝑖,𝑙,𝑟 ∙ oracle using DP and 𝑧∗ 𝑖 ′ , 𝑙 ′ ,𝑟 ∙ where either i’<i or l’<l
5. 𝑧∗ 𝑖,𝑙,𝑟 ∙ :=compress( 𝑧′ 𝑖,𝑙,𝑟 ∙ ,K)
6. endfor
end
each query = O(log logK M)
O(logS logK M) queries to 𝑧′ 𝑖,𝑙,𝑟 ∙
Running time: O(TlogS logKM log logK M)=O( 𝑇 2 𝜀 log𝑆 log𝑀 log 𝑇 log𝑀 𝜀 )
Apx. Ratio: By line 1 K2T=1+

15. Summary / Our Approach • Functional point of view
• Approximating functions via apx. sets
• Propagation of errors via Calculus of approximation
• Modular framework for deriving FPTASs
• The notion of binding constraints is of independent interest

16. Bibliography: ♦ [G+11] P. Gopalan, A. Klivans, R. Meka, D
Bibliography: ♦ [G+11] P. Gopalan, A. Klivans, R. Meka, D. Stefankoviˇc, S. Vempala, E. Vigoda, FPTAS for #Knapsack and Related Counting Problems, FOCS 2011 ♦[HKLOS14] N. Halman, D. Klabjan, C.L. Li, J. Orlin, D. Simchi-Levi, FPTASs for stochastic DPs, SIAM. Discrete Math ♦ [HS76] E. Horowitz & S. Sahni, Exact and appoximate algs. for scheduling nonidentical processors, J. ACM, ♦ [IK75] H. Ibarra & C. Kim, Fast approximation algor the knapsack and sum of subset problems, J. ACM, ♦ [KL85] R. Karp & M. Luby, Monte Carlo algs. for the planar multiterminal network reliability problem, J. Complexity, 1985 ♦ [We06] D. Weitz, Counting independent sets up to the tree threshold, STOC 2006

17. Thank you!

18. Relative Approximation
OPT – a minimization problem
OPT(x) – value of problem on instance x
A K-apx. alg. A(x) (K>1) returns A(x) ≤ K OPT(x)
A PTAS (Polynomial Time Approximation Scheme) is a (1+ ε)-apx. that runs in poly(|x|) time, e.g., O(|x|3/ε )
An FPTAS (Fully Poly. Time Approximation Scheme) is a PTAS that runs in poly(|x|,1/ε) time, e.g., O((|x|/ε)2)
FPTASs are considered as the “Holly Grail” among apx. algs. because one can get as close to opt(x) as one wants in poly. time

19. A Question
Does a value oracle for a monotone function φ:[0,...,S]→R+ admits an efficient succinct K-approximation? ( Let M = φmax . Input size = log S + log M)
Def: a K-approximation φ* is:
succinct if stored in logarithmic space, e.g., O(log S log M)
efficient if built in logarithmic time (# of oracle queries)