1,722,084 research outputs found
LA-IPM: linear algebra issues arising in interior point methods
No full textThe special issue of COAP, edited by J.Gondzio and G. Toraldo, collects contributions from some of the most expert researchers in the field of numerical optimizations, dealing with the impact of numerical linear algebra algorithms on the implementation of Interior point methods
The effect of diagonal scaling on projected gradient methods for bound constrained quadratic programming problems
No full textWe examine the e®ect of diagonal scaling on the projected gradient
method of More' and Toraldo [SIAM J. Optimization 1, (1991), pp.93-
114] when the constraints are bound constraints and the quadratic form
is positive de ̄nite. It is shown that scaling causes the method to visit
fewer faces and to minimize the function more quickly. Numerical results
are given with the journal bearing proble
P2GP - Proportionality-based 2-phase Gradient Projection method
No full textP2GP is a MATLAB code for the solution of Quadratic Programming problems with a Single Linear constraint and Bounds on the variables (SLBQPs). The problems are not required to be strictly convex. The code implements the Proportionality-based 2-phase Gradient Projection method proposed in
D. di Serafino, G. Toraldo, M. Viola, J. Barlow, A two-phase gradient method for quadratic programming problems with a single linear constraint and bounds on the variables, SIAM Journal on Optimization, 28 (4), pp. 2809-2838, ISSN: 1052-6234, doi: 10.1137/17M1128538.
It also includes SLBQPgen, a generator of SLBQPs (and BQPs), described in Section 5.1 of the aforementioned article
To the special issue dedicated to the 3rd international conference "Numerical Computations: Theory and Algorithms-NUMTA 2019" June 15-21, 2019, Isola Capo Rizzuto, Italy
No full textOUTDOOR BAR. Soluzioni sostenibili e nuovi concept per bar all'esterno - Workshop progettuale intensivo in collaborazione con IFI Industrie - 18/29 giugno 2012 - Scuola di Architettura e Design - Unicam
No full textIl Master in “Eco-design & Eco-innovazione” proponeva agli studenti un workshop progettuale intitolato
“OUTDOOR BAR. Soluzioni sostenibili e nuovi concept di prodotto per il servizio bar all’esterno" con
una particolare attenzione ai concetti di assemblabilità, disassemblabilità, modularità, impilabilità del
sistema di arredi per lo sviluppo di una nuova concezione di bar, temporanea o permanente, pensata
esclusivamente per l’outdoor in contesto di Hotel, congressi e residenze di alto livello. Il workshop è stato
organizzato e condotto in collaborazione con IFI e guidato dai proff. Lucia Pietroni e Cristiano
Toraldo di Francia. L’obiettivo della sperimentazione progettuale era incrementare l’attenzione verso il
design sostenibile proponendo lo sviluppo di prodotti/sistemi di arredo bar per la somministrazione
di cibi e bevande a basso impatto ambientale
COFFEE EXPERIENCE DESIGN. Soluzioni sostenibili e nuovi concept di prodotto per l'angolo del caffè nei locali pubblici - Workshop progettuale intensivo in collaborazione con IFI Industrie - 23 maggio/10 giugno 2011 - Scuola di Architettura e Design - Unicam
No full textIl Master in “Eco-design & Eco-innovazione” proponeva agli studenti un workshop progettuale intitolato
“COFFEE EXPERIENCE DESIGN. Soluzioni sostenibili e nuovi concept di prodotto per l’angolo del caffè nei
locali pubblici". Il progetto era incentrato sullo sviluppo di soluzioni progettuali sostenibili ed innovative, in
termini di concept, arredamento, attrezzature, tecnologie adottate e servizi offerti, per la zona di
preparazione e consumo del caffè nei locali pubblici, sia bar all’italiana che catene industriali, con particolare
attenzione ai valori e alla ritualità degli italiani nella degustazione del caffè. Il workshop è stato organizzato e condotto in collaborazione con IFI, azienda capostipite di INDUSTRIEIFI, gruppo industriale leader in soluzioni innovative per design e tecnologia nell’arredo di locali pubblici, e guidato dai Proff. Lucia Pietroni e Cristiano Toraldo di Francia
Gradient-based methods with subspace acceleration for quadratic programming problems and applications
Full text linkIn this work we propose a gradient-based framework, called "Proportionality-based Subspace Accelerated framework for Quadratic Programming" (PSAQP), for quadratic programming problems. Inspired by the Gradient Projection Conjugate Gradient (GPCG) algorithm for bound-constrained convex quadratic programming [J. J. Moré and G. Toraldo, SIAM J. Optim., 1 (1991), pp. 93{113], our approach alternates between two phases until convergence: an identification phase, which performs gradient projection iterations until either a candidate active set is identified or no reasonable progress is made, and an unconstrained minimization phase, which reduces the objective function in a suitable space defined by the identification phase. The proposed framework differs from GPCG not only because it deals with a more general class of problems, but mainly for the way it stops the minimization phase. Indeed, thanks to a component-wise reformulation of the first-order KKT conditions, we introduce a way to estimate the Lagrange multipliers which is exploited to formulate an efficient criterion to switch between the two phases. The criterion is based on a comparison between a measure of optimality in the reduced space and a measure of bindingness of the variables that are on the bounds, defined by extending the concept of proportional iterate, which was proposed by some authors for box-constrained problems.
If the objective function is bounded, every method fitting in the framework converges to a stationary point thanks to a suitable application of the gradient projection method in the identification phase. For strictly convex problems, finite convergence is proved even in the case of degeneracy.
We develop a two-phase gradient method called "Proportionality-based 2-phase Gradient Projection" (P2GP), which can be considered as a specialization of PSAQP to the case of convex and nonconvex quadratic programming problems subject to bound constraints and possibly a single linear equality constraint. P2GP, which is able to exploit efficient spectral steplength for both the two phases, shows to perform better or comparably to state-of-the-art algorithms for the same class of problems, as shown by the comparison performed on randomly generated problems and problems arising in the training of support vector machines (SVM). Motivated by the good numerical performance of P2GP in the solution of bound constrained problems, we compared it with the MPRGP algorithm [Dostál and Schöberl, 2005] as solver for the inner problems arising in an Augmented Lagrangian framework for the solution of contact mechanics problems. The performed experiments suggest that P2GP is competitive with MPRGP and, thanks to the identification properties of the gradient projection, it can result favorable when the number of active constraints at the solution is high
Recent advances in nonlinear optimization and equilibrium problems: a tribute to Marco D'Apuzzo
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