Opmerkelijke verschillen bij JA21 tussen verkiezingsprogramma en doorrekening CPB:
- wordt het €12 miljard of €500 miljoen voor wonen en infra?
- wordt het geen vliegbelasting of geen verdere verhoging?
- wordt het dividendbelasting afschaffen ja of nee?
- wordt de AOW-leeftijd versneld verhoogd ja of nee?
Oh en 14-20 kerncentrales (20 GW) kosten rond de €200 miljard, geen €800 miljoen
New far-right party in this month's Dutch elections JA21 does well in the polls after endless talk show invitations.
Turns out they misled the government agency assessing the budget effects of party platforms. The party e.g. tells voters it will 'end aviation tax', but it told PBL they're going to keep it.
https://nos.n…
Je verkiezingsbeloftes al verbreken voor de verkiezingen.
Knap, maar Eerdmans kan dat.
https://nos.nl/nieuwsuur/collectie/14005/artikel/2586491-ja21-veranderde-onvermeld-verkiezingsbeloftes-voor-doorrekening…
The maximum product of sizes of cross-\(t\)-intersecting families
Jingjun Bao, Lijun Ji
https://arxiv.org/abs/2510.11724 https://arxiv.org/pdf/2510.11724…
Abstract String Domain Defined with Word Equations as a Reduced Product (Extended Version)
Antonina Nepeivoda, Ilya Afanasyev
https://arxiv.org/abs/2510.11007 https://
If anyone else wants to run an old build of VR Paradise, from before they removed the option of female costumers for example, libfaketime works just fine to bypass the dumb “expired version” error.
#VRParadise
Riccati-ZORO: An efficient algorithm for heuristic online optimization of internal feedback laws in robust and stochastic model predictive control
Florian Messerer, Yunfan Gao, Jonathan Frey, Moritz Diehl
https://arxiv.org/abs/2511.10473 https://arxiv.org/pdf/2511.10473 https://arxiv.org/html/2511.10473
arXiv:2511.10473v1 Announce Type: new
Abstract: We present Riccati-ZORO, an algorithm for tube-based optimal control problems (OCP). Tube OCPs predict a tube of trajectories in order to capture predictive uncertainty. The tube induces a constraint tightening via additional backoff terms. This backoff can significantly affect the performance, and thus implicitly defines a cost of uncertainty. Optimizing the feedback law used to predict the tube can significantly reduce the backoffs, but its online computation is challenging.
Riccati-ZORO jointly optimizes the nominal trajectory and uncertainty tube based on a heuristic uncertainty cost design. The algorithm alternates between two subproblems: (i) a nominal OCP with fixed backoffs, (ii) an unconstrained tube OCP, which optimizes the feedback gains for a fixed nominal trajectory. For the tube optimization, we propose a cost function informed by the proximity of the nominal trajectory to constraints, prioritizing reduction of the corresponding backoffs. These ideas are developed in detail for ellipsoidal tubes under linear state feedback. In this case, the decomposition into the two subproblems yields a substantial reduction of the computational complexity with respect to the state dimension from $\mathcal{O}(n_x^6)$ to $\mathcal{O}(n_x^3)$, i.e., the complexity of a nominal OCP.
We investigate the algorithm in numerical experiments, and provide two open-source implementations: a prototyping version in CasADi and a high-performance implementation integrated into the acados OCP solver.
toXiv_bot_toot