Performative Control for Linear Dynamical Systems
Authors: Songfu Cai, Fei Han, Xuanyu Cao
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results validate our theoretical analysis. and Finally, we conduct experiments on policy-dependent stock investment risk minimization problem. The numerical results validate the effectiveness of our algorithm and theoretical analysis. |
| Researcher Affiliation | Academia | Songfu Cai , Fei Han , Xuanyu Cao Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology eesfcai@ust.hk, fhanac@connect.ust.hk, eexcao@ust.hk |
| Pseudocode | Yes | Algorithm 1 Repeated Stochastic Gradient Descent (RSGD) Input: Step sizes {ηn, 0 n N}, parameters K, H. Define M = {M : M M} . Initialize M0 M arbitrarily. 1: for n = 0, , N, do 2: Initialize JT = 0. 3: for t = 0, , T 1, do 4: Use control ut = Kxt + Mn [w]H t 1 . 5: Observe At, xt+1; compute noise wt = xt+1 Atxt But. 6: Compute the gradient Mnct (xt, ut) and update JT JT + Mnct (xt, ut) . 7: end for 8: Update Mn+1 Proj M{Mn ηn JT }. 9: end for |
| Open Source Code | No | Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [No] Justification: The paper does not provide open access to the data and code. |
| Open Datasets | No | In the simulation, we set the number of stocks L = 10 and the number of trading days T = 60. The entries of noise term wt are independently and uniformly drawn from the interval [0, 1]. For each 1 i 10 and each 0 t < 60, we first obtain v(i) t by sampling from a Gaussian distribution, i.e., v(i) t N(log(εt), 0.2), where εt denotes the sensitivity at t-th trading day. The v(i) t is then projected to the interval [ 0.6, 0.6] to obtain v(i) t in (23). |
| Dataset Splits | No | In the simulation, we set the number of stocks L = 10 and the number of trading days T = 60. (No explicit mention of training, validation, or test splits. The experiment runs on the simulated data for the full T=60 days). |
| Hardware Specification | No | The paper does not provide the type of compute workers, memory, or time of execution. (From NeurIPS Checklist). |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned. The paper describes the algorithm and parameters like 'total number iterations N = 1000, and the stepsize ηn in Algorithm 1 is set to be 0.01'. |
| Experiment Setup | Yes | In the simulation, we set the number of stocks L = 10 and the number of trading days T = 60. The initial policy M0 is randomly chosen within the feasible set M = {M : P10 i=1 ml,i = 1, 1 l 10}. The entries of noise term wt are independently and uniformly drawn from the interval [0, 1]. ... The total number iterations N = 1000, and the stepsize ηn in Algorithm 1 is set to be 0.01, 0 n 1000. |