169,683 research outputs found
Complexity and Approximation of the Continuous Network Design Problem
We revisit a classical problem in transportation, known as the continuous
(bilevel) network design problem, CNDP for short. We are given a graph for
which the latency of each edge depends on the ratio of the edge flow and the
capacity installed. The goal is to find an optimal investment in edge
capacities so as to minimize the sum of the routing cost of the induced Wardrop
equilibrium and the investment cost. While this problem is considered as
challenging in the literature, its complexity status was still unknown. We
close this gap showing that CNDP is strongly NP-complete and APX-hard, both on
directed and undirected networks and even for instances with affine latencies.
As for the approximation of the problem, we first provide a detailed analysis
for a heuristic studied by Marcotte for the special case of monomial latency
functions (Mathematical Programming, Vol.~34, 1986). Specifically, we derive a
closed form expression of its approximation guarantee for arbitrary sets S of
allowed latency functions. Second, we propose a different approximation
algorithm and show that it has the same approximation guarantee. As our final
-- and arguably most interesting -- result regarding approximation, we show
that using the better of the two approximation algorithms results in a strictly
improved approximation guarantee for which we give a closed form expression.
For affine latencies, e.g., this algorithm achieves a 1.195-approximation which
improves on the 5/4 that has been shown before by Marcotte. We finally discuss
the case of hard budget constraints on the capacity investment.Comment: 27 page
Complexity and Approximation of the Continuous Network Design Problem
We revisit a classical problem in transportation, known as the (bilevel) continuous network design problem, CNDP for short. Given a graph for which the latency of each edge depends on the ratio of the edge flow and the capacity installed, the goal is to find an optimal investment in edge capacities so as to minimize the sum of the routing costs of the induced Wardrop equilibrium and the investment costs for installing the edge's capacities. While this problem is considered to be challenging in the literature, its complexity status was still unknown. We close this gap, showing that CNDP is strongly -hard and -hard, both on directed and undirected networks and even for instances with affine latencies. As for the approximation of the problem, we first provide a detailed analysis for a heuristic studied by Marcotte for the special case of monomial latency functions [P. Marcotte, Math. Prog., 34 (1986), pp. 142--162]. We derive a closed form expression of its approximation guarantee for arbitrary sets of latency functions. We then propose a different approximation algorithm and show that it has the same approximation guarantee. Then, we prove that using the better of the two approximation algorithms results in a strictly improved approximation guarantee for which we derive a closed form expression. For affine latencies, for example, this best-of-two approach achieves an approximation factor of , which improves on the factor of that has been shown before by Marcotte
Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?
With the aim of integrating over-the-air federated learning (AirFL) and
non-orthogonal multiple access (NOMA) into an on-demand universal framework,
this paper proposes a novel reconfigurable intelligent surface (RIS)-aided
hybrid network by leveraging the RIS to flexibly adjust the signal processing
order of heterogeneous data. The objective of this work is to maximize the
achievable hybrid rate by jointly optimizing the transmit power, controlling
the receive scalar, and designing the phase shifts. Since the concurrent
transmissions of all computation and communication signals are aided by the
discrete phase shifts at the RIS, the considered problem (P0) is a challenging
mixed integer programming problem. To tackle this intractable issue, we
decompose the original problem (P0) into a non-convex problem (P1) and a
combinatorial problem (P2), which are characterized by the continuous and
discrete variables, respectively. For the transceiver design problem (P1), the
power allocation subproblem is first solved by invoking the
difference-of-convex programming, and then the receive control subproblem is
addressed by using the successive convex approximation, where the closed-form
expressions of simplified cases are derived to obtain deep insights. For the
reflection design problem (P2), the relaxation-then-quantization method is
adopted to find a suboptimal solution for striking a trade-off between
complexity and performance. Afterwards, an alternating optimization algorithm
is developed to solve the non-linear and non-convex problem (P0) iteratively.
Finally, simulation results reveal that 1) the proposed RIS-aided hybrid
network can support the on-demand communication and computation efficiently, 2)
the performance gains can be improved by properly selecting the location of the
RIS, and 3) the designed algorithms are also applicable to conventional
networks with only AirFL or NOMA users
On the Universal Approximation Property and Equivalence of Stochastic Computing-based Neural Networks and Binary Neural Networks
Large-scale deep neural networks are both memory intensive and
computation-intensive, thereby posing stringent requirements on the computing
platforms. Hardware accelerations of deep neural networks have been extensively
investigated in both industry and academia. Specific forms of binary neural
networks (BNNs) and stochastic computing based neural networks (SCNNs) are
particularly appealing to hardware implementations since they can be
implemented almost entirely with binary operations. Despite the obvious
advantages in hardware implementation, these approximate computing techniques
are questioned by researchers in terms of accuracy and universal applicability.
Also it is important to understand the relative pros and cons of SCNNs and BNNs
in theory and in actual hardware implementations. In order to address these
concerns, in this paper we prove that the "ideal" SCNNs and BNNs satisfy the
universal approximation property with probability 1 (due to the stochastic
behavior). The proof is conducted by first proving the property for SCNNs from
the strong law of large numbers, and then using SCNNs as a "bridge" to prove
for BNNs. Based on the universal approximation property, we further prove that
SCNNs and BNNs exhibit the same energy complexity. In other words, they have
the same asymptotic energy consumption with the growing of network size. We
also provide a detailed analysis of the pros and cons of SCNNs and BNNs for
hardware implementations and conclude that SCNNs are more suitable for
hardware.Comment: 9 pages, 3 figure
Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods
Training neural networks is a challenging non-convex optimization problem,
and backpropagation or gradient descent can get stuck in spurious local optima.
We propose a novel algorithm based on tensor decomposition for guaranteed
training of two-layer neural networks. We provide risk bounds for our proposed
method, with a polynomial sample complexity in the relevant parameters, such as
input dimension and number of neurons. While learning arbitrary target
functions is NP-hard, we provide transparent conditions on the function and the
input for learnability. Our training method is based on tensor decomposition,
which provably converges to the global optimum, under a set of mild
non-degeneracy conditions. It consists of simple embarrassingly parallel linear
and multi-linear operations, and is competitive with standard stochastic
gradient descent (SGD), in terms of computational complexity. Thus, we propose
a computationally efficient method with guaranteed risk bounds for training
neural networks with one hidden layer.Comment: The tensor decomposition analysis is expanded, and the analysis of
ridge regression is added for recovering the parameters of last layer of
neural networ
Online augmentation of learned grasp sequence policies for more adaptable and data-efficient in-hand manipulation
When using a tool, the grasps used for picking it up, reposing, and holding
it in a suitable pose for the desired task could be distinct. Therefore, a key
challenge for autonomous in-hand tool manipulation is finding a sequence of
grasps that facilitates every step of the tool use process while continuously
maintaining force closure and stability. Due to the complexity of modeling the
contact dynamics, reinforcement learning (RL) techniques can provide a solution
in this continuous space subject to highly parameterized physical models.
However, these techniques impose a trade-off in adaptability and data
efficiency. At test time the tool properties, desired trajectory, and desired
application forces could differ substantially from training scenarios. Adapting
to this necessitates more data or computationally expensive online policy
updates.
In this work, we apply the principles of discrete dynamic programming (DP) to
augment RL performance with domain knowledge. Specifically, we first design a
computationally simple approximation of our environment. We then demonstrate in
physical simulation that performing tree searches (i.e., lookaheads) and policy
rollouts with this approximation can improve an RL-derived grasp sequence
policy with minimal additional online computation. Additionally, we show that
pretraining a deep RL network with the DP-derived solution to the discretized
problem can speed up policy training.Comment: 7 pages (6+1 bibliography), 4 figures, 1 table, 2 algorithms, to
appear in ICRA 202
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