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Improved NP-inapproximability for 2-variable linear equations Johan Hstad Sangxia Huang Rajsekar Manokaran KTH KTH KTH Ryan ODonnell John Wright CMU CMU 2Lin x 1 = x 5 x 10 = -x 3 x 61 = -x 24 ... x 48 = -x 5 (x i = -1,1) 2Lin

  1. Improved NP-inapproximability for 2-variable linear equations Johan Håstad Sangxia Huang Rajsekar Manokaran KTH KTH KTH Ryan O’Donnell John Wright CMU CMU

  2. 2Lin x 1 = x 5 x 10 = -x 3 x 61 = -x 24 ... x 48 = -x 5 (x i = -1,1)

  3. 2Lin 2Lin( 2 ) ∈ 2Lin( q ) ≈ UniqueGames x 1 = x 5 x 10 = -x 3 x 61 = -x 24 ... x 48 = -x 5 (x i = -1,1)

  4. 2Lin 2Lin( 2 ) ∈ 2Lin( q ) ≈ UniqueGames x 1 = x 5 (Actually, simplest case of UG ) x 10 = -x 3 x 61 = -x 24 ... x 48 = -x 5 (x i = -1,1)

  5. 2Lin 2Lin( 2 ) ∈ 2Lin( q ) ≈ UniqueGames x 1 = x 5 (Actually, simplest case of UG ) x 10 = -x 3 x 61 = -x 24 Folklore wisdom: get 2Lin( 2 ) right ... and 2Lin( q ) will follow. x 48 = -x 5 (x i = -1,1)

  6. Known results Suppose val( I ) = α . Can we guarantee a solution of value C*α ?

  7. Known results Suppose val( I ) = α . Can we guarantee a solution of value C*α ? [GW] : .878-approx algorithm

  8. Known results Suppose val( I ) = α . Can we guarantee a solution of value C*α ? [GW] : .878-approx algorithm [KKMO] + [MOO] : (.878+ ε )-approx UG -hard

  9. Known results Suppose val( I ) = α . Can we guarantee a solution of value C*α ? [GW] : .878-approx algorithm [KKMO] + [MOO] : (.878+ ε )-approx UG -hard [Håstad] + [TSSW] : 16 / 17 ≈ .941-approx NP -hard

  10. Known results Suppose val( I ) = α . Can we guarantee a solution of value C*α ? [GW] : .878-approx algorithm [KKMO] + [MOO] : (.878+ ε )-approx UG -hard [Håstad] + [TSSW] : 16 / 17 ≈ .941-approx NP -hard seems we’re close, right?

  11. A different perspective... Suppose val( I ) = (1 - ε ). Can we guarantee a solution of value (1 - C*ε )?

  12. A different perspective... Suppose val( I ) = (1 - ε ). Can we guarantee a solution of value (1 - C*ε )? Def: Such an algo. gives an ( ε , C*ε )-approx.

  13. A different perspective... Suppose val( I ) = (1 - ε ). Can we guarantee a solution of value (1 - f(ε) )? Def: Such an algo. gives an ( ε , f(ε) )-approx.

  14. A different perspective... Suppose val( I ) = (1 - ε ). Can we guarantee a solution of value (1 - f(ε) )? Def: Such an algo. gives an ( ε , f(ε) )-approx. Usually called “Min-2Lin(2)-Deletion”. Let me just call this 2Lin .

  15. Unratio state of affairs [easy] : ( ε , ε )-approx NP -hard

  16. Unratio state of affairs [easy] : ( ε , ε )-approx NP -hard [Håstad] + [TSSW] : ( ε , ⁵ / ₄ * ε )-approx NP -hard

  17. Unratio state of affairs [easy] : ( ε , ε )-approx NP -hard [Håstad] + [TSSW] : ( ε , ⁵ / ₄ * ε )-approx NP -hard [KKMO] + [MOO] : ( ε , O( ε 1/2 ))-approx UG -hard

  18. Unratio state of affairs [easy] : ( ε , ε )-approx NP -hard [Håstad] + [TSSW] : ( ε , ⁵ / ₄ * ε )-approx NP -hard [KKMO] + [MOO] : ( ε , O( ε 1/2 ))-approx UG -hard [GW] : ( ε , O( ε 1/2 ))-approx algorithm

  19. Unratio state of affairs [easy] : ( ε , ε )-approx NP -hard [Håstad] + [TSSW] : ( ε , ⁵ / ₄ * ε )-approx NP -hard [KKMO] + [MOO] : ( ε , O( ε 1/2 ))-approx UG -hard [GW] : ( ε , O( ε 1/2 ))-approx algorithm asymptotically off from the truth

  20. Unratio state of affairs [easy] : ( ε , ε )-approx NP -hard [Håstad] + [TSSW] : ( ε , ⁵ / ₄ * ε )-approx NP -hard [KKMO] + [MOO] : ( ε , O( ε 1/2 ))-approx UG -hard [GW] : ( ε , O( ε 1/2 ))-approx algorithm asymptotically off from the truth [Rao] : If ( ε , O(f( q )* ε 1/2 ))-approx is NP -hard for 2Lin( q ), for f( q ) = Ω(1), then UG is true.

  21. This work [Håstad] + [TSSW] : ( ε , ⁵ / ₄ * ε )-approx NP -hard

  22. This work [Håstad] + [TSSW] : ( ε , ⁵ / ₄ * ε )-approx NP -hard [Us] : ( ε , 11 / 8 * ε )-approx NP -hard

  23. This work [Håstad] + [TSSW] : ( ε , 1.25* ε )-approx NP -hard [Us] : ( ε , 1.375* ε )-approx NP -hard

  24. This work [Håstad] + [TSSW] : ( ε , ⁵ / ₄ * ε )-approx NP -hard [Us] : ( ε , 11 / 8 * ε )-approx NP -hard

  25. This work [Håstad] + [TSSW] : ( ε , ⁵ / ₄ * ε )-approx NP -hard [Us] : ( ε , 11 / 8 * ε )-approx NP -hard Cons: - Still haven’t proven UniqueGames . 😟

  26. This work [Håstad] + [TSSW] : ( ε , ⁵ / ₄ * ε )-approx NP -hard [Us] : ( ε , 11 / 8 * ε )-approx NP -hard Cons: - Still haven’t proven UniqueGames . 😟 Pros: - First improvement since 1997.

  27. This work [Håstad] + [TSSW] : ( ε , ⁵ / ₄ * ε )-approx NP -hard [Us] : ( ε , 11 / 8 * ε )-approx NP -hard Cons: - Still haven’t proven UniqueGames . 😟 Pros: - First improvement since 1997. - Study new type of “gadget reduction”

  28. This work [Håstad] + [TSSW] : ( ε , ⁵ / ₄ * ε )-approx NP -hard [Us] : ( ε , 11 / 8 * ε )-approx NP -hard ( and more! ) Cons: - Still haven’t proven UniqueGames . 😟 Pros: - First improvement since 1997. - Study new type of “gadget reduction”

  29. Proving (ε, ⁵ / ₄ *ε)-hardness Standard two-step plan.

  30. Proving (ε, ⁵ / ₄ *ε)-hardness Standard two-step plan. [Håstad]: Given 3Lin instance I , NP -hard to distinguish - Yes : val( I ) ≥ (1 - ε ) - No : val( I ) ≤ (½ + ε )

  31. Proving (ε, ⁵ / ₄ *ε)-hardness Standard two-step plan. [Håstad]: Given 3Lin instance I , NP -hard to distinguish - Yes : val( I ) ≥ (1 - ε ) - No : val( I ) ≤ (½ + ε ) (In our language, ( ε , ½ - ε )-approxing 3Lin is NP -hard.)

  32. Proving (ε, ⁵ / ₄ *ε)-hardness Standard two-step plan. [Håstad]: Given 3Lin instance I , NP -hard to distinguish - Yes : val( I ) ≥ (1 - ε ) - No : val( I ) ≤ (½ + ε ) (In our language, ( ε , ½ - ε )-approxing 3Lin is NP -hard.) Step 2: gadget reduce 3Lin to 2Lin [TSSW]

  33. Proving (ε, ⁵ / ₄ *ε)-hardness Standard two-step plan. [Håstad]: Given 3Lin instance I , NP -hard to distinguish - Yes : val( I ) ≥ (1 - ε ) - No : val( I ) ≤ (½ + ε ) (In our language, ( ε , ½ - ε )-approxing 3Lin is NP -hard.) Step 2: gadget reduce 3Lin to 2Lin [TSSW] (see also [OW12] )

  34. 3Lin x 1 x 3 x 5 = 1 x 10 x 16 x 3 = -1 ... x 47 x 11 x 98 = -1

  35. 3Lin x 1 x 3 x 5 = 1 x 10 x 16 x 3 = -1 ... x 47 x 11 x 98 = -1

  36. 3Lin x 10 x 1 x 3 x 5 = 1 x 16 x 10 x 16 x 3 = -1 x 3 ... x 47 x 11 x 98 = -1

  37. (aux vars) 3Lin x 10 x 1 x 3 x 5 = 1 x 16 x 10 x 16 x 3 = -1 x 3 ... x 47 x 11 x 98 = -1

  38. (aux vars) 3Lin x 10 x 1 x 3 x 5 = 1 x 16 x 10 x 16 x 3 = -1 x 3 ... x 47 x 11 x 98 = -1

  39. 2Lin (aux vars) 3Lin gadget x 10 x 1 x 3 x 5 = 1 x 10 = - x 3 x 16 x 10 x 16 x 3 = -1 y 61 = -y 24 x 3 ... ... x 47 x 11 x 98 = -1 x 16 = -y 5

  40. 2Lin (aux vars) 3Lin gadget x 10 x 1 x 3 x 5 = 1 x 10 = - x 3 x 16 x 10 x 16 x 3 = -1 y 61 = -y 24 x 3 ... ... x 47 x 11 x 98 = -1 x 16 = -y 5 Final 2Lin inst: union all the gadgets

  41. 2Lin (aux vars) 3Lin gadget x 10 x 1 x 3 x 5 = 1 x 10 = - x 3 x 16 x 10 x 16 x 3 = -1 y 61 = -y 24 x 3 ... ... x 47 x 11 x 98 = -1 x 16 = -y 5 Final 2Lin inst: union all the gadgets The hope: - x i ’s satisfy 3Lin eq’n ⇒ good assgn to y i ’s

  42. 2Lin (aux vars) 3Lin gadget x 10 x 1 x 3 x 5 = 1 x 10 = - x 3 x 16 x 10 x 16 x 3 = -1 y 61 = -y 24 x 3 ... ... x 47 x 11 x 98 = -1 x 16 = -y 5 Final 2Lin inst: union all the gadgets The hope: - x i ’s satisfy 3Lin eq’n ⇒ good assgn to y i ’s - x i ’s don’t ⇒ no good assgn to y i ’s

  43. Gadgets Def: A ( c , s )-gadget

  44. Gadgets Def: A ( c , s )-gadget - x i ’s satisfy 3Lin eq’n ⇒ an assgn to y i ’s of value (1 - c )

  45. Gadgets Def: A ( c , s )-gadget - x i ’s satisfy 3Lin eq’n ⇒ an assgn to y i ’s of value (1 - c ) - x i ’s don’t ⇒ no assgn to y i ’s beats value (1 - s )

  46. Gadgets Def: A ( c , s )-gadget - x i ’s satisfy 3Lin eq’n ⇒ an assgn to y i ’s of value (1 - c ) - x i ’s don’t ⇒ no assgn to y i ’s beats value (1 - s ) [TSSW] : there is a 3Lin-to-2Lin ( ¼ , ⅜ )-gadget

  47. Gadgets Def: A ( c , s )-gadget - x i ’s satisfy 3Lin eq’n ⇒ an assgn to y i ’s of value (1 - c ) - x i ’s don’t ⇒ no assgn to y i ’s beats value (1 - s ) [TSSW] : there is a 3Lin-to-2Lin ( ¼ , ⅜ )-gadget ( ε , ½ - ε )-hardess for 3Lin

  48. Gadgets Def: A ( c , s )-gadget - x i ’s satisfy 3Lin eq’n ⇒ an assgn to y i ’s of value (1 - c ) - x i ’s don’t ⇒ no assgn to y i ’s beats value (1 - s ) [TSSW] : there is a 3Lin-to-2Lin ( ¼ , ⅜ )-gadget ( ε , ½ - ε )-hardess for 3Lin ⇒ - Yes case : ¼

  49. Gadgets Def: A ( c , s )-gadget - x i ’s satisfy 3Lin eq’n ⇒ an assgn to y i ’s of value (1 - c ) - x i ’s don’t ⇒ no assgn to y i ’s beats value (1 - s ) [TSSW] : there is a 3Lin-to-2Lin ( ¼ , ⅜ )-gadget ( ε , ½ - ε )-hardess for 3Lin ⇒ - Yes case : ¼ - No case : ½ * (¼ + ⅜)

  50. Gadgets Def: A ( c , s )-gadget - x i ’s satisfy 3Lin eq’n ⇒ an assgn to y i ’s of value (1 - c ) - x i ’s don’t ⇒ no assgn to y i ’s beats value (1 - s ) [TSSW] : there is a 3Lin-to-2Lin ( ¼ , ⅜ )-gadget ( ε , ½ - ε )-hardess for 3Lin ⇒ - Yes case : ¼ - No case : ½ * (¼ + ⅜) = 5 / 16

  51. Gadgets Def: A ( c , s )-gadget - x i ’s satisfy 3Lin eq’n ⇒ an assgn to y i ’s of value (1 - c ) - x i ’s don’t ⇒ no assgn to y i ’s beats value (1 - s ) [TSSW] : there is a 3Lin-to-2Lin ( ¼ , ⅜ )-gadget ( ε , ½ - ε )-hardess for 3Lin ⇒ - Yes case : ¼ - No case : ½ * (¼ + ⅜) = 5 / 16 = 5 / 4 * ¼

  52. How do you find gadgets? Gadgets are just 2Lin instances, so can just monkey around with small instances.

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