2,263 research outputs found
Multistage quadratic stochastic programming
Multistage stochastic programming is an important tool in medium to long term planning where there are uncertainties in the data. In this thesis, we consider a special case of multistage stochastic programming in which each subprogram is a convex quadratic program. The results are also applicable if the quadratic objectives are replaced by convex piecewise quadratic functions. Convex piecewise quadratic functions have important application in financial planning problems as they can be used as very flexible risk measures. The stochastic programming problems can be used as multi-period portfolio planning problems tailored to the need of individual investors.
Using techniques from convex analysis and sensitivity analysis, we show that each subproblem of a multistage quadratic stochastic program is a polyhedral piecewise quadratic program with convex Lipschitz objective. The objective of any subproblem is differentiable with Lipschitz gradient if all its descendent problems have unique dual variables, which can be guaranteed if the linear independence constraint qualification is satisfied. Expression for arbitrary elements of the subdifferential and generalized Hessian at a point can be calculated for quadratic pieces that are active at the point.
Generalized Newton methods with linesearch are proposed for solving multistage quadratic stochastic programs. The algorithms converge globally. If the piecewise quadratic objective is differentiable and strictly convex at the solution, then convergence is also finite.
A generalized Newton algorithm is implemented in Matlab. Numerical experiments have been carried out to demonstrate its effectiveness. The algorithm is tested on random data with 3, 4 and 5 stages with a maximum of 315 scenarios. The algorithm has also been successfully applied to two sets of test data from a capacity expansion problem and a portfolio management problem. Various strategies have been implemented to improve the efficiency of the proposed algorithm. We experimented with trust region methods with different parameters, using an advanced solution from a smaller version of the original problem and sorting the stochastic right hand sides to encourage faster convergence. The numerical results show that the proposed generalized Newton method is a highly accurate and effective method for multistage quadratic stochastic programs. For problems with the same number of stages, solution times increase linearly with the number of scenarios
Implementing Quantum Gates by Optimal Control with Doubly Exponential Convergence
We introduce a novel algorithm for the task of coherently controlling a
quantum mechanical system to implement any chosen unitary dynamics. It performs
faster than existing state of the art methods by one to three orders of
magnitude (depending on which one we compare to), particularly for quantum
information processing purposes. This substantially enhances the ability to
both study the control capabilities of physical systems within their coherence
times, and constrain solutions for control tasks to lie within experimentally
feasible regions. Natural extensions of the algorithm are also discussed.Comment: 4+2 figures; to appear in PR
Optimization Methods for Inverse Problems
Optimization plays an important role in solving many inverse problems.
Indeed, the task of inversion often either involves or is fully cast as a
solution of an optimization problem. In this light, the mere non-linear,
non-convex, and large-scale nature of many of these inversions gives rise to
some very challenging optimization problems. The inverse problem community has
long been developing various techniques for solving such optimization tasks.
However, other, seemingly disjoint communities, such as that of machine
learning, have developed, almost in parallel, interesting alternative methods
which might have stayed under the radar of the inverse problem community. In
this survey, we aim to change that. In doing so, we first discuss current
state-of-the-art optimization methods widely used in inverse problems. We then
survey recent related advances in addressing similar challenges in problems
faced by the machine learning community, and discuss their potential advantages
for solving inverse problems. By highlighting the similarities among the
optimization challenges faced by the inverse problem and the machine learning
communities, we hope that this survey can serve as a bridge in bringing
together these two communities and encourage cross fertilization of ideas.Comment: 13 page
Forward-backward truncated Newton methods for convex composite optimization
This paper proposes two proximal Newton-CG methods for convex nonsmooth
optimization problems in composite form. The algorithms are based on a a
reformulation of the original nonsmooth problem as the unconstrained
minimization of a continuously differentiable function, namely the
forward-backward envelope (FBE). The first algorithm is based on a standard
line search strategy, whereas the second one combines the global efficiency
estimates of the corresponding first-order methods, while achieving fast
asymptotic convergence rates. Furthermore, they are computationally attractive
since each Newton iteration requires the approximate solution of a linear
system of usually small dimension
Efficient Algorithms for Optimal Control of Quantum Dynamics: The "Krotov'' Method unencumbered
Efficient algorithms for the discovery of optimal control designs for
coherent control of quantum processes are of fundamental importance. One
important class of algorithms are sequential update algorithms generally
attributed to Krotov. Although widely and often successfully used, the
associated theory is often involved and leaves many crucial questions
unanswered, from the monotonicity and convergence of the algorithm to
discretization effects, leading to the introduction of ad-hoc penalty terms and
suboptimal update schemes detrimental to the performance of the algorithm. We
present a general framework for sequential update algorithms including specific
prescriptions for efficient update rules with inexpensive dynamic search length
control, taking into account discretization effects and eliminating the need
for ad-hoc penalty terms. The latter, while necessary to regularize the problem
in the limit of infinite time resolution, i.e., the continuum limit, are shown
to be undesirable and unnecessary in the practically relevant case of finite
time resolution. Numerical examples show that the ideas underlying many of
these results extend even beyond what can be rigorously proved.Comment: 19 pages, many figure
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