C164 polys
 

@RichD
Whichever sieves better:
1), 2) your two optimized polys with c5: 507276
3) a poly from the previous post with c5: 26520, MurphyE=7.84174299e-13
4) the following:
[code]
skew: 12836425.42475
c0: 25856493264761217061935195606283306368192
c1: 6326482042018892445700837102628232
c2: -1275115621670963641931801774
c3: -68937977941239457607
c4: 2824507851702
c5: 270360
Y0: -44763913952811633346728714315065
Y1: 2034346315112017744613
# MurphyE = 8.49516139e-13
[/code][code]
Y0: -7622840675301422537341421219942
Y1: 12652746799867972895307127
c0: 2149832126757950068889458496081778632520
c1: -1734166109900699659629694625743171
c2: -716880307153393821752580234
c3: 235223214816767138605
c4: 23611568792748
c5: 1604160
skew: 2474367.50642
# lognorm 52.52, E 44.31, alpha -8.21 (proj -2.06), 3 real roots
# MurphyE=8.15312875e-13
[/code][code]
Y0: -36738740386570381527362211917435
Y1: 465834562234854517381
c0: 284080175959798104806007442266959256
c1: 10537086803210505807173354552891
c2: -16769631209785781541508878
c3: -38919992610746925953
c4: 12178605847374
c5: 4358160
skew: 1158802.64144
# lognorm 49.51, E 44.07, alpha -5.43 (proj -1.81), 5 real roots
# MurphyE=7.96840757e-13
[/code][code]
Y0: -86535798637147693153875157356696
Y1: 457221668087373328621
c0: -16414682532805128372992337183492213396225
c1: 7352607907685487993058034991846466
c2: 66066455737418963233021016
c3: -57876216454909350638
c4: -761206584471
c5: 30060
skew: 20633714.76228
# lognorm 51.49, E 44.81, alpha -6.68 (proj -1.65), 5 real roots
# MurphyE=7.78303052e-13
[/code][code]
Y0: -61757048606717445628740476518990
Y1: 6309450685733927849
c0: -10573243064628632316174101212002249051604
c1: -6029915082227380575565765535897783
c2: 2412226174653249896058761962
c3: 161791055856423756167
c4: 13419499853106
c5: -324720
skew: 11958575.88987
# lognorm 53.22, E 44.72, alpha -8.49 (proj -2.39), 3 real roots
# MurphyE=7.66862595e-13
[/code]Good luck sieving!

C164 msieve poly
 

@RichD
Msieve's picks:
[code]
# norm 7.340642e-016 alpha -7.909979 e 8.502e-013 rroots 3
skew: 12714361.75
c0: 32270475048490642492715814348988436317925
c1: 2066560285664734495069985116792751
c2: -1538716814726769395758706123
c3: -45818084889392790239
c4: 4858454519502
c5: 270360
Y0: -44760853032624643187477796958392
Y1: 2034346315112017744613
[/code]
[code]
# norm 6.471419e-016 alpha -7.451729 e 7.842e-013 rroots 3
skew: 16512871.30
c0: 16077670067078941985624611483028414609850
c1: -8140656491233749699543175443802191
c2: -140544823434647317073523818
c3: 96882341081827114105
c4: 1088529219310
c5: 26520
Y0: -81124832138067832288070206668952
Y1: 79052040992330600806697
[/code]
[code]
# norm 6.390678e-016 alpha -6.567593 e 7.790e-013 rroots 3
skew: 6848418.66
c0: 283632916500004468328693081177564129675
c1: 762889040051482290057040346138895
c2: -175563973611860392148292609
c3: -59658823617527448143
c4: -3774867548898
c5: 270360
Y0: -44773845462671266063060442178186
Y1: 2034346315112017744613
[/code]
[code]
# norm 6.298911e-016 alpha -6.695572 e 7.731e-013 rroots 5
skew: 21263051.24
c0: 642262155811169745491402771063028131795
c1: 7357179148574911253311199467581242
c2: 844079834825948777495005900
c3: -31279540867027020326
c4: -1605669839871
c5: 30060
Y0: -86538367545319832086494734360374
Y1: 457221668087373328621
[/code]
[code]
# norm 6.321968e-016 alpha -8.492478 e 7.670e-013 rroots 3
skew: 11978560.40
c0: 13187198211382762256681178465790865233050
c1: 3126436864448652719229169590136315
c2: -2712975110531336851551388012
c3: -191204239953620352983
c4: -12498214010706
c5: 324720
Y0: 61757045026520605219994861449524
Y1: -6309450685733927849
[/code]

C207
 

I've done some searching already, and this is the best I have:
[CODE]183724913753361567376492453926230323715345031792001208551707422272237266349933302881515963689094609592709968359761386456940894165548045328984901031969851838708505435691913321760214712695688550560374318369687[/CODE]

[CODE]#expecting poly E from 1.95e-015 to > 2.24e-015
# norm 2.262499e-020 alpha -8.404310 e 1.404e-015 rroots 5
skew: 2796538203.22
c0: -7668944838191204153747071831251033656420858660632800
c1: 68724916140885781979136020357469304420727260
c2: -10741463294346121539895727780440916
c3: -78176439213970987416623261
c4: 515945478020706
c5: 821916
Y0: -11745408433223050782080932895561571583039
Y1: 2433524106205299767[/CODE]

I've searched to A1 (C5) = 2M and am continuing. Any better polynomials are appreciated.

[QUOTE=wombatman;455627]I've searched to A1 (C5) = 2M and am continuing. Any better polynomials are appreciated.[/QUOTE]

I'll start at 10M on msieve-GPU, and report my findings in a few days; with stage 1 norm set to 1e30, msieve says "testing piece 2 of 7", so overlapping my range is only slightly likely to turn up the same polys.

Gonna work above 15 M on gpu, with a norm for stage 1 at 7e29 and stage 2 at 8e28.

C207
 

@wombatman @VBCurtis @firejuggler
Could you please zip and pass me a link to your c207.dat.p files? I'll run CADO magic on them. I promise a better E score (not necessarily a higher test-sieve speed, unfortunately).

The dat.p file? Are you sure you don't want the dat.ms?
Anyway, a very slight improvement on Wombatman's
[code]
R0: -6563265022568138472573627741423786813426
R1: 14737700014198380373
A0: -415122934870735733701421696894789415316686983654165
A1: 2255344676137178694925802343372585156679804
A2: 23002701211984284542594351645944502
A3: -11944592993638683469789636
A4: -67091824749020289
A5: 15085800
skew 619250538.72, size 1.893e-020, alpha -7.832, combined = 1.425e-015 rroots = 5
[/code]the line was
[code]
15085800 -51624436302059289 -60632246937025826543510440 28519157529846713344704397551817 14245447456034261295098916589150880049710 -1992560008032780496276009820420695848968627635 14737700014198380373 -6563265022565116375887546937034932420369 -1.63 3.975482e+027
[/code]

[QUOTE=VBCurtis;455629]I'll start at 10M on msieve-GPU, and report my findings in a few days; with stage 1 norm set to 1e30, msieve says "testing piece 2 of 7", so overlapping my range is only slightly likely to turn up the same polys.[/QUOTE]

[QUOTE=firejuggler;455631]Gonna work above 15 M on gpu, with a norm for stage 1 at 7e29 and stage 2 at 8e28.[/QUOTE]

[QUOTE=firejuggler;455644]The dat.p file? Are you sure you don't want the dat.ms?
Anyway, a very slight improvement on Wombatman's
[code]
R0: -6563265022568138472573627741423786813426
R1: 14737700014198380373
A0: -415122934870735733701421696894789415316686983654165
A1: 2255344676137178694925802343372585156679804
A2: 23002701211984284542594351645944502
A3: -11944592993638683469789636
A4: -67091824749020289
A5: 15085800
skew 619250538.72, size 1.893e-020, alpha -7.832, combined = 1.425e-015 rroots = 5
[/code]the line was
[code]
15085800 -51624436302059289 -60632246937025826543510440 28519157529846713344704397551817 14245447456034261295098916589150880049710 -1992560008032780496276009820420695848968627635 14737700014198380373 -6563265022565116375887546937034932420369 -1.63 3.975482e+027
[/code][/QUOTE]

Thanks!

I'm also running CADO from 10M to 15M; the default P (depth, a little like stage 1 norm) setting takes about 6 thread-days per million, so I'll give it a few cores and let it run a week or so. I have 10-11M set to run first, so I'll have some CADO polys tomorrow.

C207
 

@firejuggler @wombatman @VBCurtis

> The dat.p file? Are you sure you don't want the dat.ms?

The dat.p file please. Although you can pack and send me both :)
Many fairly good polys would have the same values of (c5,Y1), they would come from the same (c5,Y1) family so to say.
Size-optimization in Msieve doesn't change c5, size-optimization in CADO can scale c5-->k*c5. Msieve never produces c5<0 from scratch, CADO does it routinely. Also, root-optimization in Msieve and CADO are done very differently sometimes.
The high E score doesn't necessarily guarantee that the poly sieves the best, it just shows that the poly comes from a good (c5,Y1) family. We can take good families, mutate them in both Msieve and CADO to produce many different polys (by E score, alpha, lognorm; not by c4-c0, Y0) with high E, and then sort and test-sieve those.
I just need to see all good (c5,Y1) pairs that your Msieve/CADO discovered.

[QUOTE=Max0526;455326]@RichD
Whichever sieves better:
1), 2) your two optimized polys with c5: 507276
3) a poly from the previous post with c5: 26520, MurphyE=7.84174299e-13
4) the following:...[/QUOTE]

Thanks for all the work Max. The best score provided the best yield. The lowest score had the best times. My thinking, I'm leaning towards the best yielding because the search won't go as far into the Q range which produces much slower relations. I need to check a few more at different ranges. Then five more polys were posted. I'll eventually get to those too.