Such papers is just a way to get a degree, zero new ideas.
And all this is not important actually.
The main problem that you won't get better than K=1.7 this way even if you use 1000 instead of 3-4-5-7 kangs.
K=1.7 is boring, really. It's like solving puzzles #6x
Why don't you try this? https://www.iacr.org/archive/pkc2010/60560372/60560372.pdf
Going this way (a long way btw) you can "invent" something interesting and get real K (not in theory but real) around 1.2 if you are smart enough
I call this approach "mirrored" kangs.
But, of course, this way is not so easy as "normal" kangs that always jump in one direction.
Just a tip for you because it seems you read some papers instead of creating magic circles/code as most people here
Though I doubt that you will break #135, it's always fun to invent and discover so you can spend a lot of time having fun...
And all this is not important actually.
The main problem that you won't get better than K=1.7 this way even if you use 1000 instead of 3-4-5-7 kangs.
K=1.7 is boring, really. It's like solving puzzles #6x
Why don't you try this? https://www.iacr.org/archive/pkc2010/60560372/60560372.pdf
Going this way (a long way btw) you can "invent" something interesting and get real K (not in theory but real) around 1.2 if you are smart enough
I call this approach "mirrored" kangs.But, of course, this way is not so easy as "normal" kangs that always jump in one direction.
Just a tip for you because it seems you read some papers instead of creating magic circles/code as most people here
Though I doubt that you will break #135, it's always fun to invent and discover so you can spend a lot of time having fun...
Thanks, we all already know about that paper, and I discussed it a few times in some of my posts. I also implemented it (even in my public Python kangaroo script) and it indeed brings C to ~ 1.25 - 1.45 but at the expense of much more complex logic, since we can have cycles in the side by side walks, which need to be handled. In addition, that method completely ignores the cost of having to create new starting points, which is not something to be ignored (in fact, is very very costly). This problem is also mentioned in the 3 & 4 kangaroo methods (which was actually a follow up to the paper you cited).
There is also a more recent "fix" to the Gaudry-Schost method (but it is written in Chinese) which brings down C a little bit more, by refining the wild set bounds.
However even though C is lower than 1.7, the actual runtime is worse, because of the additional restarts, and cycle detection logic. That means it requires more computing power and additional registers, at the machine level. If well optimized, it can beat 4-Kangaroo on a CPU + RAM, but on a GPU the added requirements (cycle detection, more registers) do not reach the efficiency (throughput) of Kangaroo method, which is simple and direct: jump forward, check if DP, repeat.
I don't intend on ruining someone's tenure, but I also highly suspect that the paper about 5 and 7 kangaroos was not reviewed by supervisors or even by the peer community..
I also discovered that C can go as low as 1.05 if we also allow 1.05 sqrt(N) stored items, with 3 kangaroos alone, and only addition group operations. And still people believe BSGS would work faster. for whatever reason, with TB of RAM.. I let them continue believing that.