Abstract: A purpose of the present invention is to achieve highly accurate optimization for a submodular function when a policy is optimized without preparing data for machine learning. An optimization apparatus (100) according to the present invention includes a determination unit (110) that determines one or more execution policies from a predetermined policy in an objective function having diminishing marginal utility and convexity, a reward acquisition unit (120) that acquires reward, which is an execution result in the objective function for the determined execution policy, a calculation unit (130) that calculates an update rate of the policy based on the reward, and an update unit (140) that updates the policy based on the update rate.

Abstract: An information processing device 1X mainly includes an output means 15X. The output means outputs, as a predetermined number of solutions, candidates in which a minimum value of a distance between the solutions satisfies a predetermined condition among candidates used to select the predetermined number of solutions, when outputting the predetermined number of solutions, from a plurality of solutions in an optimization problem.

Abstract: Based on a relation model representing a relationship between first data and second data, second data in an evaluation period is calculated from first data in the evaluation period. An evaluation value pertaining to the evaluation period is calculated by using an evaluation model and the calculated second data in the evaluation period, the evaluation model including the second data as a parameter. First data in the evaluation period in a case in which the calculated evaluation value increases are determined.

Abstract: An information processing apparatus includes: a selection section for selecting a subset Xt?[n] of a set [n] in a certain round t?[T] with reference to an observation value of an objective function in a round t?1; and an output section for outputting information indicating the subset Xt?[n] which has been selected by the selection section, the selection section selecting the subset Xt?[n] so that an asymptotic behavior of an expected value of a regret ?t?[T]ft(Xt)??t?[T]ft(X*), which is expressed using an observation value ft(Xt) of an objective function in each round t?[T] and a comparative solution X*, is bounded from above by an upper limit value A(?,n,C) which depends at least on a gap indicator ? in a stochastic model and on a corruption indicator C indicating an adversarial corruption of the stochastic model.

Abstract: A display system including: a document content acquisition unit that acquires, on a basis of bibliographic information that includes storage location information indicating a storage location of each of a plurality of electronic documents to be displayed and is related to a bibliographic matter of each of the plurality of electronic documents, document content information indicating a content of at least one electronic document among the plurality of electronic documents stored in the storage location; and a display unit that displays, on a basis of correspondence information in which each of a plurality of items set in advance is associated with a description position in the at least one electronic document, and the document content information, content indicated by the document content information corresponding to the description position in the at least one electronic document, on a user terminal of a user for each item.

Abstract: A display system including: a document content acquisition unit that acquires, on a basis of bibliographic information that includes storage location information indicating a storage location of each of a plurality of electronic documents to be displayed and is related to a bibliographic matter of each of the plurality of electronic documents, document content information indicating a content of at least one electronic document among the plurality of electronic documents stored in the storage location; and a display unit that displays, on a basis of correspondence information in which each of a plurality of items set in advance is associated with a description position in the at least one electronic document, and the document content information, content indicated by the document content information corresponding to the description position in the at least one electronic document, on a user terminal of a user for each item.

Abstract: An optimization apparatus includes: a selection unit that selects, as a correction value, an element having a magnitude equal to or smaller than a predetermined value from among convex hulls of a policy set; an acquisition unit that acquires a result of execution of a second policy executed in a second round, the second round being a round a predetermined round before a first round for executing a first policy that is determined from among the policy set; a calculation unit that calculates an estimated value of a loss vector in the execution of the policy based on the result of the execution and the correction value selected in the second round; an update unit that updates a first probability distribution based on the estimated value; and a determination unit that determines a policy for a next round based on the updated first probability distribution.

Abstract: An optimization apparatus includes: a selection unit that selects, as a correction value, an element having a magnitude equal to or smaller than a predetermined value from among convex hulls of a policy set; an acquisition unit that acquires a result of execution of a second policy executed in a second round, the second round being a round a predetermined round before a first round for executing a first policy that is determined from among the policy set; a calculation unit that calculates an estimated value of a loss vector in the execution of the policy based on the result of the execution and the correction value selected in the second round; an update unit that updates a first probability distribution based on the estimated value; and a determination unit that determines a policy for a next round based on the updated first probability distribution.

Abstract: A spacecraft includes: a main-reflection unit configured to reflect and emit a radio wave outside, a sub-reflection unit configured to face the main-reflection unit, a radiator arranged to face the sub-reflection unit and configured to radiate the radio wave in a direction of the sub-reflection unit, a main body configured to be able to accommodate at least one part of the sub-reflection unit therein, and a delivery device connected to the sub-reflection unit and configured to deliver the sub-reflection unit, at least one part of which is accommodated in the main body, to a position where the sub-reflection unit is able to reflect the radio wave radiated from the radiator to the main-reflection unit and cause the main-reflection unit to radiate the radio wave outside.

Abstract: An optimization apparatus includes: a selection unit that selects, as a correction value, an element having a magnitude equal to or smaller than a predetermined value from among convex hulls of a policy set; an acquisition unit that acquires a result of execution of a second policy executed in a second round, the second round being a round a predetermined round before a first round for executing a first policy that is determined from among the policy set; a calculation unit that calculates an estimated value of a loss vector in the execution of the policy based on the result of the execution and the correction value selected in the second round; an update unit that updates a first probability distribution based on the estimated value; and a determination unit that determines a policy for a next round based on the updated first probability distribution.

Abstract: Spacecraft and antenna apparatus that can be more easily deployed from a stored state can include: a main-reflection unit including a plurality of ribs formed to be deployable in a stored state in which the ribs are folded and a sheet body provided between a plurality of the ribs and configured to be capable of reflecting a radio wave radiated from a radiator and emitting the radio wave outside, and a restriction member configured to restrict deployment of the plurality of ribs in the stored state, and release the restriction by operation of a restriction release member different from the main-reflection unit.

Abstract: An optimization apparatus (100) includes a setting unit (110) that sets a predetermined non-linear objective function, a policy determination unit (120) that determines a policy to be executed in online optimization in a bandit problem, based on the non-linear objective function, a policy execution unit (130) that acquires a reward as an execution result of the determined policy, an update rate determination unit (140) that determines an update rate of the non-linear objective function by a multiplicative weight update method, based on the acquired reward and the non-linear objective function, and an update unit (150) that updates the non-linear objective function, based on the update rate.

Abstract: A non-transitory computer-readable storage medium storing a navigation monitoring program that causes at least one computer to execute a process, the process includes acquiring direction information that indicates a direction of a vessel and position information that indicates a position of the vessel; and predicting whether or not the vessel navigates along a course by inputting the acquired direction information and the acquired position information to a prediction model generated by machine learning by using direction information and position information for each of a plurality of vessels that has navigated in the past and a correct answer label that indicates whether or not each of the plurality of vessels navigates along a course.

Abstract: To enable selection of useful vector sequence a1,a2, . . . ,aT in a bandit linear optimization algorithm for which a fixed strategy is ineffective, an information processing apparatus (1) includes a vector selection unit (11) that selects a vector at in each round t?[T] (T is any natural number) from a subset A of a d-dimensional vector space Rd (d is any natural number). The vector selection unit (11) uses l1,l2, . . . ,lT?Rd as loss vectors to select the vector at in each round t such that an asymptotic behavior of an expected value of tracking regret R(u)=?t?[T]ltTat??t?[T]ltTut with respect to any comparative vector sequence u1,u2, . . . ,uT?A or an asymptotic behavior ignoring logarithmic factors of the expected value of the tracking regret R(u) is constrained from above by a preset function A(d,T,P), where P is a natural number not less than 1 given by P=|{t?[T?1]|ut?ut+1}|.

Abstract: A purpose of the present invention is to achieve highly accurate optimization for a submodular function when a policy is optimized without preparing data for machine learning. An optimization apparatus (100) according to the present invention includes a determination unit (110) that determines one or more execution policies from a predetermined policy in an objective function having diminishing marginal utility and convexity, a reward acquisition unit (120) that acquires reward, which is an execution result in the objective function for the determined execution policy, a calculation unit (130) that calculates an update rate of the policy based on the reward, and an update unit (140) that updates the policy based on the update rate.

Abstract: In an optimization device, an acquisition means acquires a reward obtained by executing a certain policy. An updating means updates a probability distribution of the policy based on the obtained reward. Here, the updating means uses a weighted sum of the probability distributions updated in a past as a constraint. A determination means determines the policy to be executed, based on the updated probability distributions.

Abstract: In order to enable derivation of useful subsets X1, X2, . . . , XT also for an online submodular optimization problem for which a fixed strategy is not effective, an information processing apparatus (1) includes: an objective function setting unit (11) that sets, as an objective function ft in each round t?[T], a submodular function on a power set 2S of a set S consisting of n elements; and a subset sequence derivation unit (12) that derives a subset sequence X1, X2, . . . , XT?2S in which an expected value of regret ?t?[T]ft(Xt)??t?[T]ft(Xt*) with respect to any benchmark X1*, X2*, . . . , Xt*?2S satisfying ?t?[T?1]dH(Xt*, Xt+1*) is not more than an upper limit Max (n,T,V).

Abstract: An optimization apparatus includes: a selection unit that selects, as a correction value, an element having a magnitude equal to or smaller than a predetermined value from among convex hulls of a policy set; an acquisition unit that acquires a result of execution of a second policy executed in a second round, the second round being a round a predetermined round before a first round for executing a first policy that is determined from among the policy set; a calculation unit that calculates an estimated value of a loss vector in the execution of the policy based on the result of the execution and the correction value selected in the second round; an update unit that updates a first probability distribution based on the estimated value; and a determination unit that determines a policy for a next round based on the updated first probability distribution.

Abstract: A non-transitory computer-readable storage medium storing a navigation monitoring program that causes at least one computer to execute a process, the process includes acquiring direction information that indicates a direction of a vessel and position information that indicates a position of the vessel; and predicting whether or not the vessel navigates along a course by inputting the acquired direction information and the acquired position information to a prediction model generated by machine learning by using direction information and position information for each of a plurality of vessels that has navigated in the past and a correct answer label that indicates whether or not each of the plurality of vessels navigates along a course.

Abstract: A learning unit 81 generates a plurality of sample groups from samples to be used for learning, and generates a plurality of prediction models while inhibiting overlapping of a sample group to be used for learning among the generated sample groups. An optimization unit 82 generates an objective function based on an explained variable predicted by the prediction model and based on a constraint condition for optimization, and optimizes a generated objective function. An evaluation unit 83 evaluates an optimization result by using a sample group that has not been used in learning of a prediction model used for generating an objective function targeted for the optimization.