IA Scholar Query: Solving the Lagrangian Dual when the Number of Constraints is Fixed.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgThu, 01 Dec 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440A logic-based Benders decomposition for microscopic railway timetable planning
https://scholar.archive.org/work/nxeu2ezyrjhwjcr44kgibmmjiu
Railway timetable planning is one of the key factors in the successful operation of a railway network. The timetable must satisfy all operational restrictions at a microscopic representation of the railway network, while maximizing transportation capacity for passengers and freight. The microscopic planning of a railway timetable is an NP-Hard problem, difficult to solve for large-scale railway networks, such as those of entire countries. In this work, we propose a logic Benders decomposition approach to solve the problem of microscopic railway timetable planning. Our decomposition exploits the typical structure of a railway with dense networks around major hubs and sparse connections in-between hubs. A logic Benders cut is designed, which we are able to compute effectively for all decomposed problems within our considered structure, using a SAT based algorithm we developed. Moreover, an aggregation scheme for Benders cuts is proposed to speed up the iterative process. Experiments on real-world cases of the Swiss Federal Railways show a clear improvement in scalability compared to a variety of benchmarks including centralized approaches.Florin Leutwiler, Francesco Cormanwork_nxeu2ezyrjhwjcr44kgibmmjiuThu, 01 Dec 2022 00:00:00 GMTImproved VMD-ELM Algorithm for MEMS Gyroscope of Temperature Compensation Model Based on CNN-LSTM and PSO-SVM
https://scholar.archive.org/work/prq5kgkmtbfxdboq25gvpysqde
The micro-electro-mechanical system (MEMS) gyroscope is a micro-mechanical gyroscope with low cost, small volume, and good reliability. The working principle of the MEMS gyroscope, which is achieved through Coriolis, is different from traditional gyroscopes. The MEMS gyroscope has been widely used in the fields of micro-inertia navigation systems, military, automotive, consumer electronics, mobile applications, robots, industrial, medical, and other fields in micro-inertia navigation systems because of its advantages of small volume, good performance, and low price. The material characteristics of the MEMS gyroscope is very significant for its data output, and the temperature determines its accuracy and limits its further application. In order to eliminate the effect of temperature, the MEMS gyroscope needs to be compensated to improve its accuracy. This study proposed an improved variational modal decomposition—extreme learning machine (VMD-ELM) algorithm based on convolutional neural networks—long short-term memory (CNN-LSTM) and particle swarm optimization—support vector machines (PSO-SVM). By establishing a temperature compensation model, the gyro temperature output signal is optimized and reconstructed, and the gyro output signal with better accuracy is obtained. The VMD algorithm separates the gyro output signal and divides the gyro output signal into low-frequency signals, mid-frequency signals, and high-frequency signals according to the different signal frequencies. Once again, the PSO-SVM model is constructed by the mid-frequency temperature signal to find the temperature error. Finally, the signal is reconstructed through the ELM neural network algorithm, and then, the gyro output signal after noise is obtained. Experimental results show that, by using the improved method, the output of the MEMS gyroscope ranging from −40 to 60 °C reduced, and the temperature drift dramatically declined. For example, the factor of quantization noise (Q) reduced from 1.2419 × 10−4 to 1.0533 × 10−6, the factor of bias instability (B) reduced from 0.0087 to 1.8772 × 10−4, and the factor of random walk of angular velocity (N) reduced from 2.0978 × 10−5 to 1.4985 × 10−6. Furthermore, the output of the MEMS gyroscope ranging from 60 to −40 °C reduced. The factor of Q reduced from 2.9808 × 10−4 to 2.4430 × 10−6, the factor of B reduced from 0.0145 to 7.2426 × 10−4, and the factor of N reduced from 4.5072 × 10−5 to 1.0523 × 10−5. The improved algorithm can be adopted to denoise the output signal of the MEMS gyroscope to improve its accuracy.Xinwang Wang, Huiliang Caowork_prq5kgkmtbfxdboq25gvpysqdeThu, 24 Nov 2022 00:00:00 GMTA proposal for 3d quantum gravity and its bulk factorization
https://scholar.archive.org/work/cycwrprrijgplluxitariaiu7e
Recent progress in AdS/CFT has provided a good understanding of how the bulk spacetime is encoded in the entanglement structure of the boundary CFT. However, little is known about how spacetime emerges directly from the bulk quantum theory. We address this question in an effective 3d quantum theory of pure gravity, which describes the high temperature regime of a holographic CFT. This theory can be viewed as a q-deformation and dimensional uplift of JT gravity. Using this model, we show that the Bekenstein-Hawking entropy of a two-sided black hole equals the bulk entanglement entropy of gravitational edge modes. In the conventional Chern-Simons description, these black holes correspond to Wilson lines in representations of (2,ℝ)⊗(2,ℝ). We show that the correct calculation of gravitational entropy suggests we should interpret the bulk theory as an extended topological quantum field theory associated to the quantum semi-group ^+_q(2,ℝ)⊗^+_q(2,ℝ). Our calculation suggests an effective description of bulk microstates in terms of collective, anyonic degrees of freedom whose entanglement leads to the emergence of the bulk spacetime.Thomas G. Mertens, Joan Simón, Gabriel Wongwork_cycwrprrijgplluxitariaiu7eWed, 23 Nov 2022 00:00:00 GMTKnots, minimal surfaces and J-holomorphic curves
https://scholar.archive.org/work/jxpgkptcxzhltcsxxsrvo3lkxm
Let K be a knot in the 3-sphere, viewed as the ideal boundary of hyperbolic 4-space ℍ^4. We prove that the number of minimal discs in ℍ^4 with ideal boundary K is a knot invariant. I.e. the number is finite and doesn't change under isotopies of K. In fact this gives a family of knot invariants, indexed by an integer describing the extrinsic topology of how the disc sits in ℍ^4. These invariants can be seen as Gromov–Witten invariants counting J-holomorphic discs in the twistor space Z of ℍ^4. Whilst Gromov–Witten theory suggests the general scheme for defining the invariants, there are substantial differences in how this must be carried out in our situation. These are due to the fact that the geometry of both ℍ^4 and Z becomes singular at infinity, and so the J-holomorphic curve equation is degenerate, rather than elliptic, at the boundary. This means that both the Fredholm and compactness arguments involve completely new features.Joel Finework_jxpgkptcxzhltcsxxsrvo3lkxmWed, 23 Nov 2022 00:00:00 GMTTailored Presolve Techniques in Branch-and-Bound Method for Fast Mixed-Integer Optimal Control Applications
https://scholar.archive.org/work/hr5zalzew5durek3lufp5c45zu
Mixed-integer model predictive control (MI-MPC) can be a powerful tool for modeling hybrid control systems. In case of a linear-quadratic objective in combination with linear or piecewise-linear system dynamics and inequality constraints, MI-MPC needs to solve a mixed-integer quadratic program (MIQP) at each sampling time step. This paper presents a collection of block-sparse presolve techniques to efficiently remove decision variables, and to remove or tighten inequality constraints, tailored to mixed-integer optimal control problems (MIOCP). In addition, we describe a novel heuristic approach based on an iterative presolve algorithm to compute a feasible but possibly suboptimal MIQP solution. We present benchmarking results for a C code implementation of the proposed BB-ASIPM solver, including a branch-and-bound (B&B) method with the proposed tailored presolve techniques and an active-set based interior point method (ASIPM), compared against multiple state-of-the-art MIQP solvers on a case study of motion planning with obstacle avoidance constraints. Finally, we demonstrate the computational performance of the BB-ASIPM solver on the dSPACE Scalexio real-time embedded hardware using a second case study of stabilization for an underactuated cart-pole with soft contacts.Rien Quirynen, Stefano Di Cairanowork_hr5zalzew5durek3lufp5c45zuWed, 23 Nov 2022 00:00:00 GMTSome remarks on non-singular spherically symmetric space-times
https://scholar.archive.org/work/crpbimwoxrhblgyqvqy2qrt5ki
A short review on spherically symmetric static regular black holes and spherically symmetric non singular cosmological space-time is presented. Several models of regular black holes, including new ones, are considered. First, a large class of regular black holes having an inner de Sitter core with the related issue of Cauchy horizon is investigated. Then, black bounce space-times, where the Cauchy horizon and therefore the related instabilities are absent, are discussed as valid alternatives of regular black holes with inner de Sitter core. Friedman-Lemaitre-Robertson-Walker space-times admitting regular bounce solutions are also discussed. In the general analysis concerning the presence or absence of singularities in the equations of motion, the role of a theorem due to Osgood is stressed.Lorenzo Sebastiani, Sergio Zerbiniwork_crpbimwoxrhblgyqvqy2qrt5kiWed, 23 Nov 2022 00:00:00 GMTQuantum Gravity in 30 Questions
https://scholar.archive.org/work/mllid5xuefabziu5uy4uz7avs4
Quantum gravity is the missing piece in our understanding of the fundamental interactions today. Given recent observational breakthroughs in gravity, providing a quantum theory for what lies beyond general relativity is more urgent than ever. However, the complex history of quantum gravity and the multitude of available approaches can make it difficult to get a grasp of the topic and its main challenges and opportunities. We provide a guided tour of quantum gravity in the form of 30 questions, aimed at a mixed audience of learners and practitioners. The issues covered range from basic motivational and background material to a critical assessment of the status quo and future of the subject. The emphasis is on structural issues and our current understanding of quantum gravity as a quantum field theory of dynamical geometry beyond perturbation theory. We highlight the identification of quantum observables and the development of effective numerical tools as critical to future progress.Renate Loll, Giuseppe Fabiano, Domenico Frattulillo, Fabian Wagnerwork_mllid5xuefabziu5uy4uz7avs4Wed, 23 Nov 2022 00:00:00 GMTFuzzy Adaptive Energy Management Strategy for a Hybrid Agricultural Tractor Equipped with HMCVT
https://scholar.archive.org/work/i2g4i2lgirg6rnj3dth3ehaooq
In order to solve the problem of high fuel consumption and poor emission performance in high horsepower tractors, a parallel hybrid tractor system was designed using a dual power source of an engine and motor matched with a hydro-mechanical continuously variable transmission (HMCVT). An equivalent fuel consumption minimization strategy (ECMS) was used for power distribution of this hybrid system. To address the problem of poor adaptability of the equivalence factor to different working cycles in the conventional ECMS, a fuzzy adaptive equivalent fuel consumption minimization strategy (FA-ECMS) was proposed. A fuzzy PI controller based on battery SOC (State of Charge) feedback was designed to adjust the equivalence factor in real time, so as to achieve adaptive control of the equivalence factor. The physical model of the system was built by SimulationX, and the model of the control strategy was built using Matlab/Simulink. Two typical cycles of tractor plowing and road transportation were simulated. Under ECMS, the fuel consumption of the hybrid agricultural tractor was 14.3 L and 1.19 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.75% and 60.32%, respectively. Under FA-ECMS, the hybrid farm tractor consumed 13.34 L and 1.13 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.27% and 60.17%, respectively. The results showed that, with the introduction of a fuzzy PI controller to dynamically adjust the equivalence factor, the overall fuel consumption was reduced by 6.71% and 5.04%, respectively, and the battery power maintenance performance was improved. The designed control strategy could achieve a more reasonable power distribution between the engine and motor while maintaining the balance of the battery SOC.Zhen Zhu, Lingxin Zeng, Long Chen, Rong Zou, Yingfeng Caiwork_i2g4i2lgirg6rnj3dth3ehaooqWed, 23 Nov 2022 00:00:00 GMTRobust Markov decision processes under parametric transition distributions
https://scholar.archive.org/work/jmp4tni6ebgjvfhaffgwmakw5a
This paper considers robust Markov decision processes under parametric transition distributions. We assume that the true transition distribution is uniquely specified by some parametric distribution, and explicitly enforce that the worst-case distribution from the model is uniquely specified by a distribution in the same parametric family. After formulating the parametric robust model, we focus on developing algorithms for carrying out the robust Bellman updates required to complete robust value iteration. We first formulate the update as a linear program by discretising the ambiguity set. Since this model scales poorly with problem size and requires large amounts of pre-computation, we develop two additional algorithms for solving the robust Bellman update. Firstly, we present a cutting surface algorithm for solving this linear program in a shorter time. This algorithm requires the same pre-computation, but only ever solves the linear program over small subsets of the ambiguity set. Secondly, we present a novel projection-based bisection search algorithm that completely eliminates the need for discretisation and does not require any pre-computation. We test our algorithms extensively on a dynamic multi-period newsvendor problem under binomial and Poisson demands. In addition, we compare our methods with the non-parametric phi-divergence based methods from the literature. We show that our projection-based algorithm completes robust value iteration significantly faster than our other two parametric algorithms, and also faster than its non-parametric equivalent.Ben Black, Trivikram Dokka, Christopher Kirkbridework_jmp4tni6ebgjvfhaffgwmakw5aWed, 23 Nov 2022 00:00:00 GMTJoint Secure Communication and Radar Beamforming: A Secrecy-Estimation Rate-Based Design
https://scholar.archive.org/work/tkh4doqz3rdynjaxysa4nbxpci
This paper considers transmit beamforming in dual-function radar-communication (DFRC) system, where a DFRC transmitter simultaneously communicates with a communication user and detects a malicious target with the same waveform. Since the waveform is embedded with information, the information is risked to be intercepted by the target. To address this problem, physical-layer security technique is exploited. By using secrecy rate and estimation rate as performance measure for communication and radar, respectively, three secrecy rate maximization (SRM) problems are formulated, including the SRM with and without artificial noise (AN), and robust SRM. For the SRM beamforming, we prove that the optimal beamformer can be computed in closed form. For the AN-aided SRM, by leveraging alternating optimization similar closed-form solution is obtained for the beamformer and the AN covariance matrix. Finally, the imperfect CSI of the target is also considered under the premise of a moment-based random phase-error model on the direction of arrival at the target. Simulation results demonstrate the efficacy and robustness of the proposed designs.Rong Wen, Ying Zhang, Qiang Li, Youxi Tangwork_tkh4doqz3rdynjaxysa4nbxpciWed, 23 Nov 2022 00:00:00 GMTLattice Quantum Villain Hamiltonians: Compact scalars, U(1) gauge theories, fracton models and Quantum Ising model dualities
https://scholar.archive.org/work/nhyipvejxfc27k7evpfzqumyli
We construct Villain Hamiltonians for compact scalars and abelian gauge theories. The Villain integers are promoted to integral spectrum operators, whose canonical conjugates are naturally compact scalars. Further, depending on the theory, these conjugate operators can be interpreted as (higher-form) gauge fields. If a gauge symmetry is imposed on these dual gauge fields, a natural constraint on the Villain operator leads to the absence of defects (e.g. vortices, monopoles,...). These lattice models therefore have the same symmetry and anomaly structure as their corresponding continuum models. Moreover they can be formulated in a way that makes the well-know dualities look manifest, e.g. a compact scalar in 2d has a T-duality, in 3d is dual to a U(1) gauge theory, etc. We further discuss the gauged version of compact scalars on the lattice, its anomalies and solution, as well as a particular limit of the gauged XY model at strong coupling which reduces to the transverse-field Ising model. The construction for higher-form gauge theories is similar. We apply these ideas to the constructions of some models which are of interest to fracton physics, in particular the XY-plaquette model and the tensor gauge field model. The XY-plaquette model in 2+1d coupled to a tensor gauge fields at strong gauge coupling is also exactly described by a transverse field quantum J_1-J_2 Ising model with J_1=2J_2, and discuss the phase structure of such models.Lucca Fazza, Tin Sulejmanpasicwork_nhyipvejxfc27k7evpfzqumyliWed, 23 Nov 2022 00:00:00 GMTChaos in Matrix Gauge Theories with Massive Deformations
https://scholar.archive.org/work/6v2gp34s7rfdjc7v4ahzvnaxdy
Starting from an 𝑆𝑈 (𝑁) matrix quantum mechanics model with massive deformation terms and by introducing an ansatz configuration involving fuzzy four-and two-spheres with collective time dependence, we obtain a family of effective Hamiltonians, 𝐻 𝑛 , (𝑁 = 1 6 (𝑛 + 1) (𝑛 + 2) (𝑛 + 3)) and examine their emerging chaotic dynamics. Through numerical work, we model the variation of the largest Lyapunov exponents as a function of the energy and find that they vary either as ∝ (𝐸 − (𝐸 𝑛 ) 𝐹 ) 1/4 or ∝ 𝐸 1/4 , where (𝐸 𝑛 ) 𝐹 stand for the energies of the unstable fixed points of the phase space. We use our results to put upper bounds on the temperature above which the Lyapunov exponents comply with the Maldacena-Shenker-Stanford (MSS) bound, 2𝜋𝑇, and below which it will eventually be violated.Seckin Kurkcuoglu, K. Baskan, O. Oktay, C. Tasciwork_6v2gp34s7rfdjc7v4ahzvnaxdyWed, 23 Nov 2022 00:00:00 GMTQCD Axion Kinetic Misalignment without Prejudice
https://scholar.archive.org/work/i6aaumd25je53moc4k77kyixi4
The axion field, the angular direction of the complex scalar field associated with the spontaneous symmetry breaking of the Peccei-Quinn (PQ) symmetry, could have originated with initial non-zero velocity. The presence of a non-zero angular velocity resulting from additional terms in the potential that explicitly break the PQ symmetry has important phenomenological consequences such as a modification of the axion mass with respect to the conventional PQ framework or an explanation for the observed matter-antimatter asymmetry. We elaborate further on the consequences of the "kinetic misalignment" mechanism, assuming that axions form the entirety of the dark matter abundance. The kinetic misalignment mechanism possesses a weak limit in which the axion field starts to oscillate at the same temperature as in the conventional PQ framework, and a strong limit corresponding to large initial velocities which effectively delay the onset of oscillations. Following a UV-agnostic approach, we show how this scenario impacts the formation of axion miniclusters, and we sketch the details of these substructures along with potential detecting signatures.Basabendu Barman, Nicolás Bernal, Nicklas Ramberg, Luca Visinelliwork_i6aaumd25je53moc4k77kyixi4Wed, 23 Nov 2022 00:00:00 GMTOff-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction
https://scholar.archive.org/work/li77py3ogjaa5cf6r5xx4zavya
Improving the sample efficiency of reinforcement learning algorithms requires effective exploration. Following the principle of optimism in the face of uncertainty (OFU), we train a separate exploration policy to maximize the approximate upper confidence bound of the critics in an off-policy actor-critic framework. However, this introduces extra differences between the replay buffer and the target policy regarding their stationary state-action distributions. To mitigate the off-policy-ness, we adapt the recently introduced DICE framework to learn a distribution correction ratio for off-policy RL training. In particular, we correct the training distribution for both policies and critics. Empirically, we evaluate our proposed method in several challenging continuous control tasks and show superior performance compared to state-of-the-art methods. We also conduct extensive ablation studies to demonstrate the effectiveness and rationality of the proposed method.Jiachen Li, Shuo Cheng, Zhenyu Liao, Huayan Wang, William Yang Wang, Qinxun Baiwork_li77py3ogjaa5cf6r5xx4zavyaTue, 22 Nov 2022 00:00:00 GMTBregman iterative regularization using model functions for nonconvex nonsmooth optimization
https://scholar.archive.org/work/vsfeui3bsfdxnf22kmghn5zx4m
In this paper, we propose a new algorithm called ModelBI by blending the Bregman iterative regularization method and the model function technique for solving a class of nonconvex nonsmooth optimization problems. On one hand, we use the model function technique, which is essentially a first-order approximation to the objective function, to go beyond the traditional Lipschitz gradient continuity. On the other hand, we use the Bregman iterative regularization to generate solutions fitting certain structures. Theoretically, we show the global convergence of the proposed algorithm with the help of the Kurdyka-Łojasiewicz property. Finally, we consider two kinds of nonsmooth phase retrieval problems and propose an explicit iteration scheme. Numerical results verify the global convergence and illustrate the potential of our proposed algorithm.Haoxing Yang, Hui Zhang, Hongxia Wang, Lizhi Chengwork_vsfeui3bsfdxnf22kmghn5zx4mTue, 22 Nov 2022 00:00:00 GMTCornering Large-N_c QCD with Positivity Bounds
https://scholar.archive.org/work/4ontlqy7uve7hjz7s4ip63cln4
The simple analytic structure of meson scattering amplitudes in the large-N_c limit, combined with positivity of the spectral density, provides precise predictions on low-energy observables. Building upon previous studies, we explore the allowed regions of chiral Lagrangian parameters and meson couplings to pions. We reveal a structure of kinks at all orders in the chiral expansion and develop analytical tools to show that kinks always correspond to amplitudes with a single light pole. We build (scalar- and vector-less) deformations of the Lovelace-Shapiro and Coon UV-complete amplitudes, and show that they lie close to the boundaries. Moreover, constraints from crossing-symmetry imply that meson couplings to pions become smaller as their spin increases, providing an explanation for the success of Vector Meson Dominance and holographic QCD. We study how these conclusions depend on assumptions about the high-energy behavior of amplitudes. Finally, we emphasize the complementarity between our results and Lattice computations in the exploration of large-N_c QCD.Clara Fernandez, Alex Pomarol, Francesco Riva, Francesco Sciottiwork_4ontlqy7uve7hjz7s4ip63cln4Tue, 22 Nov 2022 00:00:00 GMTLevel-S^2fM: Structure from Motion on Neural Level Set of Implicit Surfaces
https://scholar.archive.org/work/dur5qyoi4ndzfkihtha6fqpsse
This paper presents a neural incremental Structure-from-Motion (SfM) approach, Level-S^2fM. In our formulation, we aim at simultaneously learning coordinate MLPs for the implicit surfaces and the radiance fields, and estimating the camera poses and scene geometry, which is mainly sourced from the established keypoint correspondences by SIFT. Our formulation would face some new challenges due to inevitable two-view and few-view configurations at the beginning of incremental SfM pipeline for the optimization of coordinate MLPs, but we found that the strong inductive biases conveying in the 2D correspondences are feasible and promising to avoid those challenges by exploiting the relationship between the ray sampling schemes used in volumetric rendering and the sphere tracing of finding the zero-level set of implicit surfaces. Based on this, we revisit the pipeline of incremental SfM and renew the key components of two-view geometry initialization, the camera pose registration, and the 3D points triangulation, as well as the Bundle Adjustment in a novel perspective of neural implicit surfaces. Because the coordinate MLPs unified the scene geometry in small MLP networks, our Level-S^2fM treats the zero-level set of the implicit surface as an informative top-down regularization to manage the reconstructed 3D points, reject the outlier of correspondences by querying SDF, adjust the estimated geometries by NBA (Neural BA), finally yielding promising results of 3D reconstruction. Furthermore, our Level-S^2fM alleviated the requirement of camera poses for neural 3D reconstruction.Yuxi Xiao and Nan Xue and Tianfu Wu and Gui-Song Xiawork_dur5qyoi4ndzfkihtha6fqpsseTue, 22 Nov 2022 00:00:00 GMTSimulating Hydrodynamics in Cosmology with CRK-HACC
https://scholar.archive.org/work/izk557sgozbmhl5o6okwjnq2iq
We introduce CRK-HACC, an extension of the Hardware/Hybrid Accelerated Cosmology Code (HACC), to resolve gas hydrodynamics in large-scale structure formation simulations of the universe. The new framework couples the HACC gravitational N-body solver with a modern smoothed particle hydrodynamics (SPH) approach called CRKSPH. Conservative Reproducing Kernel SPH utilizes smoothing functions that exactly interpolate linear fields while manifestly preserving conservation laws (momentum, mass, and energy). The CRKSPH method has been incorporated to accurately model baryonic effects in cosmology simulations - an important addition targeting the generation of precise synthetic sky predictions for upcoming observational surveys. CRK-HACC inherits the codesign strategies of the HACC solver and is built to run on modern GPU-accelerated supercomputers. In this work, we summarize the primary solver components and present a number of standard validation tests to demonstrate code accuracy, including idealized hydrodynamic and cosmological setups, as well as self-similarity measurements.Nicholas Frontiere, J.D. Emberson, Michael Buehlmann, Joseph Adamo, Salman Habib, Katrin Heitmann, Claude-André Faucher-Giguèrework_izk557sgozbmhl5o6okwjnq2iqTue, 22 Nov 2022 00:00:00 GMTIMPROVED INTELLIGENT BASE TECHNIQUE FOR PATH AND SOLUTION IN ROBOTIC USING PREWILL EDGE DETECTION PARADIGM
https://scholar.archive.org/work/p7byppccpraujep77zm4j37sli
The major contribution in this research paper is concerned with the development of a humanoid robot using edge detection technique which selects features of the principal parts of the object and eliminates parts that are not necessary. The developed prototype of the motorized robot shows that robots in real life exhibit some level of intelligence. In the design process, the model of a humanoid robot was developed first using the simulink library tools in Matlab/Simulink environment. This research paper has improved autonomous path finding robots by incorporating very powerful and well-structured program/codes that gives the robot the ability to predict and make smart decision lending to efficient execution of desired assignment (picking of dirt in the surrounding).Result shows that the developed robot has simplified the way No robot interacts with object thereby saving cost and energy. A motorized autonomous path finding robot was designed and constructed to demonstrate the working principle of robot. The motorized robot and the humanoid robot have the capability to detect obstacle along its part at 30cm away from the obstacle. When the robot is switched on, it initializes after which forward movement until it gets 30cm closer to an obstacle it then stops, reverse backwards and then change direction. When it moves 30cm close to another obstacle, it stops reverse backwards, then turns left or right to another direction, and will continue to behave that way until the power button is switched off.E.C. Aneke, Chukwuagu M.Iwork_p7byppccpraujep77zm4j37sliTue, 22 Nov 2022 00:00:00 GMTSelf-Supervised Primal-Dual Learning for Constrained Optimization
https://scholar.archive.org/work/2n5ycjps2ncfjjbn4qtnspeoze
This paper studies how to train machine-learning models that directly approximate the optimal solutions of constrained optimization problems. This is an empirical risk minimization under constraints, which is challenging as training must balance optimality and feasibility conditions. Supervised learning methods often approach this challenge by training the model on a large collection of pre-solved instances. This paper takes a different route and proposes the idea of Primal-Dual Learning (PDL), a self-supervised training method that does not require a set of pre-solved instances or an optimization solver for training and inference. Instead, PDL mimics the trajectory of an Augmented Lagrangian Method (ALM) and jointly trains primal and dual neural networks. Being a primal-dual method, PDL uses instance-specific penalties of the constraint terms in the loss function used to train the primal network. Experiments show that, on a set of nonlinear optimization benchmarks, PDL typically exhibits negligible constraint violations and minor optimality gaps, and is remarkably close to the ALM optimization. PDL also demonstrated improved or similar performance in terms of the optimality gaps, constraint violations, and training times compared to existing approaches.Seonho Park, Pascal Van Hentenryckwork_2n5ycjps2ncfjjbn4qtnspeozeTue, 22 Nov 2022 00:00:00 GMT