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About 10,000 papers for “Continuous optimization”

CONTINUOUS OPTIMIZATION

Heredia, F.-Javier (Francisco Javier), Castro Pérez, Jordi · QRU Quaderns de Recerca en Urbanisme · 2017

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Cited by 0Open access

Continuous Optimization

Brian Fahs, Todd Rafacz, Sanjay J. Patel et al. · ACM SIGARCH Computer Architecture News · 2005

This paper presents a hardware-based dynamic optimizer that continuously optimizes an applicationýs instruction stream. In continuous optimization, dataflow optimizations are performed using simple, table-based hardware placed in the rename stage of the processor pipeline. The continuous optimizer reduces dataflow height by performing constant propagation, reassociation, redundant load elimination, store forwarding, and silent store removal. To enhance the impact of the optimizations, the optimizer integrates values generated by the execution units back into the optimization process. Continuous optimization allows instructions with input values known at optimization time to be executed in the optimizer, leaving less work for the out-of-order portion of the pipeline. Continuous optimization can detect branch mispredictions earlier and thus reduce the misprediction penalty. In this paper, we present a detailed description of a hardware optimizer and evaluate it in the context of a contemporary microarchitecture running current workloads. Our analysis of SPECint, SPECfp, and mediabench workloads reveals that a hardware optimizer can directly execute 33% of instructions, resolve 29% of mispredicted branches, and generate addresses for 76% of memory operations. These positive effects combine to provide speed ups in the range 0.99 to 1.27.

Cited by 28

Continuous Optimization

Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong · Cambridge University Press eBooks · 2020

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Cited by 1

Continuous Optimization

Alain Pétrowski, Sana Ben-Hamida, Sanjay Patel et al. · 2017

P u b lic re p o rtin g b u rae n to r this co lle ctio n ot inform ation is e stim a te d to a v e ra g e 1 h o u r p e r re sp on se, in c lu d in g th e tim e to r re vie w in g in stru ctio n s, s e a rch in g e xisting d ata so urces, g a th e n n g a nd m a in ta in in g the data n ee de d , a nd co m p letin g a nd re vie w in g th e c o lle c tio n of in form atio n . S end co m m e nt re g a rd in g th is b urde n e s tim a te s o r a ny o th e r a sp e ct o f th is co lle ctio n of in fo rm a tio n , in clu din g s u g g e stio n s fo r re d ucin g th is b urde n, to W a s h in g to n H e a d q u a rte rs S e rvices. D ire c to ra te fo r in fo rm a tio n O p e ra tio n s a nd R eports. 1215 Je ffe rs o n O avis H ig h w ay. S u ite 1204. A rlin g to n . V A 222 02 -4 3 02 . a n d to th e O ffice o f M a n a g e m e n t a n d B udget.

Cited by 1Open access

Optimization techniques I continuous optimization

Cerf, Max

This book in two volumes provides an overview of continuous, discrete and functional optimization techniques. This first volume is devoted to continuous optimization, which deals with problems with real variables, without or with constraints. After a reminder of the optimality conditions and their geometrical interpretation, the topics covered are:-gradient-free algorithms that can be applied to any type of function;-unconstrained algorithms based on Newton-type descent methods;-algorithms with constraints: penalization, primal, dual and primal-dual methods;-linear programming with the simplex method and interior point methods. The emphasis is on understanding the principles rather than on mathematical rigor. Each concept or algorithm is accompanied by a detailed example to help you grasp the main ideas. This book is the result of 30 years of experience and is intended for students, researchers and engineers wishing to acquire a general knowledge in the field of optimization

Cited by 0

Continuous Optimization Problems

Tianjun Liao, Marco A. Montes De Oca, Thomas Stützle et al. · 2011

The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is not responsible for any use that might be made of data appearing in this publication. An Incremental ACOR with Local Search for

Cited by 0

Continuous Dynamic Optimization

Walid Tfaili · InTech eBooks · 2011

In this chapter we introduce a new ant colony algorithm aimed at continuous and dynamic problems. To deal with the changes in the dynamic problems, the diversification in the ant population is maintained by attributing to every ant a repulsive electrostatic charge, that allows to keep ants at some distance from each other. The algorithm is based on a continuous ant colony algorithm that uses a weighted continuous Gaussian distribution, instead of the discrete distribution, used to solve discrete problems. Experimental results and comparisons with two competing methods available in the literature show best performances of our new algorithm called CANDO on a set of multimodal dynamic continuous test functions. To find the shortest way between the colony and a source of food, ants adopt a particular collective organization technique (see figure 1). The first algorithm inspired from ant colonies, called ACO (refer to Dorigo & Gambardella (1997), Dorigo & Gambardella (2002)), was proposed as a multi-agent approach to solve hard combinatorial optimization problems. It was applied to discrete problems like the traveling salesman, routing and communication problems.

Cited by 2Open access

Continuous Query Optimization

Avnur, Ron, Author, Hellerstein, Joseph M, Author · UC Berkeley · 1999

In large federated and shared-nothing databases, resources can exhibit widely fluctuating characteristics. Assumptions made at the time a query is submitted will rarely hold throughout the duration of query processing. As a result, traditional static query optimization and execution techniques are ineffective in these environments. <p>In this paper we introduce a query processing mechanism called an eddy, which continuously reorders operators in a query plan as it runs. We characterize the moments of symmetry during which pipelined joins can be easily reordered, and the synchronization barriers that require inputs from different sources to be coordinated. By combining eddies with join algorithms appropriate to large-scale environments, we merge the optimization and execution phases of query processing, allowing each tuple to have a flexible ordering of the query operators. This flexibility is controlled by a combination of fluid dynamics and a simple learning algorithm. Our initial implementation demonstrates promising results, with eddies performing nearly as well as a static optimizer/executor in static scenarios, and adapting to run-time changes in the execution environment

Cited by 3

Continuous Integration Optimization Experiment

Research Team · Zenodo (CERN European Organization for Nuclear Research) · 2026

Automated testing of gradient descent optimization on benchmark functions (Rosenbrock, Sphere, Quadratic) with known analytical solutions. This experiment demonstrates reproducible optimization testing with CI/CD integration.

Cited by 0Open access

Quadratic Continuous Quantum Optimization

Sascha Mücke, Thore Gerlach, Nico Piatkowski · ArXiv.org · 2025

Quantum annealers can solve QUBO problems efficiently but struggle with continuous optimization tasks like regression due to their discrete nature. We introduce Quadratic Continuous Quantum Optimization (QCQO), an anytime algorithm that approximates solutions to unconstrained quadratic programs via a sequence of QUBO instances. Rather than encoding real variables as binary vectors, QCQO implicitly represents them using continuous QUBO weights and iteratively refines the solution by summing sampled vectors. This allows flexible control over the number of binary variables and adapts well to hardware constraints. We prove convergence properties, introduce a step size adaptation scheme, and validate the method on linear regression. Experiments with simulated and real quantum annealers show that QCQO achieves accurate results with fewer qubits, though convergence slows on noisy hardware. Our approach enables quantum annealing to address a wider class of continuous problems.

Cited by 0Open access