43 research outputs found
Algorithmes d'approximation pour des programmes linéaires et les problÚmes de Packing avec des contraintes géometriques
In this thesis we approach several problems with approximation algorithms; these are feasibility problems as well as optimization problems. In Chapter 1 we give a brief introduction into the general paradigm of approximation algorithms, motivate the problems, and give an outline of the
thesis. In Chapter 2, we discuss two algorithms to approximately generate a feasible solution of the mixed packing and covering problem which is
a model from convex optimization. This problem includes a large class of
linear programs. The algorithms generate approximately feasible solutions
within O(M(ln M+epsilon^{-2} ln epsilon^{-1})) and
O(M epsilon{-2} ln (M epsilon^{-1}))3/2+\epsilon$ for any epsilon>0. With an interesting argument, the approximation ratio for both problems is
refined to exactly 3/2. We also point out an interesting relation of scheduling with fixed jobs to Bin Packing. As in Chapter 4, the results are in a certain sense best possible.
Finally, in Chapter 6, we conclude
with some remarks and open research problems
Advances and Novel Approaches in Discrete Optimization
Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled âAdvances and Novel Approaches in Discrete Optimizationâ. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms
The Computational Complexity of Linear Optics
We give new evidence that quantum computers -- moreover, rudimentary quantum
computers built entirely out of linear-optical elements -- cannot be
efficiently simulated by classical computers. In particular, we define a model
of computation in which identical photons are generated, sent through a
linear-optical network, then nonadaptively measured to count the number of
photons in each mode. This model is not known or believed to be universal for
quantum computation, and indeed, we discuss the prospects for realizing the
model using current technology. On the other hand, we prove that the model is
able to solve sampling problems and search problems that are classically
intractable under plausible assumptions. Our first result says that, if there
exists a polynomial-time classical algorithm that samples from the same
probability distribution as a linear-optical network, then P^#P=BPP^NP, and
hence the polynomial hierarchy collapses to the third level. Unfortunately,
this result assumes an extremely accurate simulation. Our main result suggests
that even an approximate or noisy classical simulation would already imply a
collapse of the polynomial hierarchy. For this, we need two unproven
conjectures: the "Permanent-of-Gaussians Conjecture", which says that it is
#P-hard to approximate the permanent of a matrix A of independent N(0,1)
Gaussian entries, with high probability over A; and the "Permanent
Anti-Concentration Conjecture", which says that |Per(A)|>=sqrt(n!)/poly(n) with
high probability over A. We present evidence for these conjectures, both of
which seem interesting even apart from our application. This paper does not
assume knowledge of quantum optics. Indeed, part of its goal is to develop the
beautiful theory of noninteracting bosons underlying our model, and its
connection to the permanent function, in a self-contained way accessible to
theoretical computer scientists.Comment: 94 pages, 4 figure
Hexarray: A Novel Self-Reconfigurable Hardware System
Evolvable hardware (EHW) is a powerful autonomous system for adapting and finding solutions within a changing environment. EHW consists of two main components: a reconfigurable hardware core and an evolutionary algorithm. The majority of prior research focuses on improving either the reconfigurable hardware or the evolutionary algorithm in place, but not both. Thus, current implementations suffer from being application oriented and having slow reconfiguration times, low efficiencies, and less routing flexibility. In this work, a novel evolvable hardware platform is proposed that combines a novel reconfigurable hardware core and a novel evolutionary algorithm.
The proposed reconfigurable hardware core is a systolic array, which is called HexArray. HexArray was constructed using processing elements with a redesigned architecture, called HexCells, which provide routing flexibility and support for hybrid reconfiguration schemes. The improved evolutionary algorithm is a genome-aware genetic algorithm (GAGA) that accelerates evolution. Guided by a fitness function the GAGA utilizes context-aware genetic operators to evolve solutions. The operators are genome-aware constrained (GAC) selection, genome-aware mutation (GAM), and genome-aware crossover (GAX). The GAC selection operator improves parallelism and reduces the redundant evaluations. The GAM operator restricts the mutation to the part of the genome that affects the selected output. The GAX operator cascades, interleaves, or parallel-recombines genomes at the cell level to generate better genomes. These operators improve evolution while not limiting the algorithm from exploring all areas of a solution space.
The system was implemented on a SoC that includes a programmable logic (i.e., field-programmable gate array) to realize the HexArray and a processing system to execute the GAGA. A computationally intensive application that evolves adaptive filters for image processing was chosen as a case study and used to conduct a set of experiments to prove the developed system robustness. Through an iterative process using the genetic operators and a fitness function, the EHW system configures and adapts itself to evolve fitter solutions. In a relatively short time (e.g., seconds), HexArray is able to evolve autonomously to the desired filter.
By exploiting the routing flexibility in the HexArray architecture, the EHW has a simple yet effective mechanism to detect and tolerate faulty cells, which improves system reliability. Finally, a mechanism that accelerates the evolution process by hiding the reconfiguration time in an âevolve-while-reconfigureâ process is presented. In this process, the GAGA utilizes the array routing flexibility to bypass cells that are being configured and evaluates several genomes in parallel